Outcomes Research in Review

Study Overview

Objective. To identify psychosocial predictors of weight loss maintenance in a multi-site clinical trial following a group-based weight loss program.

Design. Secondary analysis of a 2-phase randomized controlled trial. The first phase was a 6-month group-based weight loss program and the second phase was a 30-month trial comparing weight loss maintenance strategies.

Setting and participants. The patients studied were participants in the Weight Loss Maintenance trial [1], which was conducted at 4 US clinical centers. Eligible participants were overweight and obese adults with a BMI between 25 to 45 who were actively taking medication for hypertension, dyslipidemia, or both. 1685 patients were recruited into the weight-loss phase, and those who lost at least 4 kg (n = 1032) were then randomly assigned to 1 of the 3 maintenance arms: (a) self-directed with minimal intervention (control), (b) interactive technology that consisted of unlimited, interactive study website access, or (c) personal contact consisting of monthly, personalized telephone calls and quarterly face-to-face contact with a study interventionist. There was 1 death in each treatment group, so a total of 1029 participants were included in the analyses.

Main outcome measures. The researchers examined the associations between psychosocial variables and weight change outcomes at 12 and 30 months. Patients completed 5 self-report measures at the time of randomization into phase 2 of the study: a social support and exercise survey, a social support and eating habits survey, the SF-36, the Patient Health Questionnaire Depression Scale, and the Perceived Stress Scale.

Results. Of the 1029 participants initially included for analyses, 2 failed to provide complete data on the social support scales and 2 were identified as outliers at both 12 and 30 months. This resulted in a final sample size of 1025 participants; 63% were women, 61% were non-Latino white, and 38% were black. The mean age was 55.6 years. All groups regained weight, with the personal contact group having the least amount of gain. However, the mean weight at 30 months remained significantly lower than the mean weight at entry into phase 1.

Only 3 psychosocial variables were significantly related to weight loss at 12 and 30 months. At both time marks, less weight regain was associated with higher SF-36 mental health composite scores (P < 0.01). Interestingly, for black participants at the 12-month mark, more weight regain was associated with higher exercise encouragement from friends (P < 0.05). At 30 months, more weight regain was associated with friends’ encouragement for healthy eating (P < 0.05).

Conclusion. The psychosocial variables that were self-reported upon entering phase 2 may predict the ability of an individual to maintain weight loss at 12 and 30 months. The significant, complex interactions between these variables, race, sex, and treatment interventions need to be further studied for proper incorporation into a weight loss maintenance program.

Commentary

The case for the obesity epidemic has been long established. Unfortunately, the causes for weight gain can be complicated and multifactorial. Factors associated with weight gain include nonmodifiable factors such as age, sex, and race as well as modifiable factors like lifestyle, eating habits, and perceived stress [2]. The CDC states that about 78.6 million American adults are overweight or obese [3], and about 25% to 40% of US adults attempt to lose weight each year [4]. It is unclear what proportion of those who lose weight are successful at maintaining their weight loss [5,6].

Researchers and practitioners alike understand that maintaining weight loss is difficult. Most studies on weight regain have focused on biological and lifestyle factors [7]. This article did a good job in detailing gaps in knowledge and supporting the need for further study of psychosocial variables. The results demonstrated complex, interactive relationships between multiple factors. Three psychosocial variables were found to be statistically significant in relation to weight loss, but more significant relationships were found between race, perceived stress, and weight loss.

As a secondary analysis, this study carries the strengths of the initial study, including a 30-month study duration. In addition, this study was randomized and included 3 different treatment arms. Lastly, there was a large representation of black participants, and the authors suggested that the results may offer and initial characterization of this population.

A limitation of this study was the use of self-reported data, which may be subject to be bias and be less reliable than direct measured data. Also, these measures were apparently taken only once prior to the beginning of phase 2 with no rationale provided. Psychosocial variables such as social support, quality of life, and perceived stress are dynamic and cannot be accurately encapsulated in isolated moments in time. The sample’s diversity was also lacking as there were few Hispanics and no Asian
participants.

Applications for Clinical Practice

A few significant, interactive relationships were discovered in this examination and require further study. Continued research and a better understanding of these complex relationships are needed.

—Angela M. Godwin Beoku-Betts, MSN, FNP–BC

Study Overview

Objective. To identify psychosocial predictors of weight loss maintenance in a multi-site clinical trial following a group-based weight loss program.

Design. Secondary analysis of a 2-phase randomized controlled trial. The first phase was a 6-month group-based weight loss program and the second phase was a 30-month trial comparing weight loss maintenance strategies.

Setting and participants. The patients studied were participants in the Weight Loss Maintenance trial [1], which was conducted at 4 US clinical centers. Eligible participants were overweight and obese adults with a BMI between 25 to 45 who were actively taking medication for hypertension, dyslipidemia, or both. 1685 patients were recruited into the weight-loss phase, and those who lost at least 4 kg (n = 1032) were then randomly assigned to 1 of the 3 maintenance arms: (a) self-directed with minimal intervention (control), (b) interactive technology that consisted of unlimited, interactive study website access, or (c) personal contact consisting of monthly, personalized telephone calls and quarterly face-to-face contact with a study interventionist. There was 1 death in each treatment group, so a total of 1029 participants were included in the analyses.

Main outcome measures. The researchers examined the associations between psychosocial variables and weight change outcomes at 12 and 30 months. Patients completed 5 self-report measures at the time of randomization into phase 2 of the study: a social support and exercise survey, a social support and eating habits survey, the SF-36, the Patient Health Questionnaire Depression Scale, and the Perceived Stress Scale.

Results. Of the 1029 participants initially included for analyses, 2 failed to provide complete data on the social support scales and 2 were identified as outliers at both 12 and 30 months. This resulted in a final sample size of 1025 participants; 63% were women, 61% were non-Latino white, and 38% were black. The mean age was 55.6 years. All groups regained weight, with the personal contact group having the least amount of gain. However, the mean weight at 30 months remained significantly lower than the mean weight at entry into phase 1.

Only 3 psychosocial variables were significantly related to weight loss at 12 and 30 months. At both time marks, less weight regain was associated with higher SF-36 mental health composite scores (P < 0.01). Interestingly, for black participants at the 12-month mark, more weight regain was associated with higher exercise encouragement from friends (P < 0.05). At 30 months, more weight regain was associated with friends’ encouragement for healthy eating (P < 0.05).

Conclusion. The psychosocial variables that were self-reported upon entering phase 2 may predict the ability of an individual to maintain weight loss at 12 and 30 months. The significant, complex interactions between these variables, race, sex, and treatment interventions need to be further studied for proper incorporation into a weight loss maintenance program.

Commentary

The case for the obesity epidemic has been long established. Unfortunately, the causes for weight gain can be complicated and multifactorial. Factors associated with weight gain include nonmodifiable factors such as age, sex, and race as well as modifiable factors like lifestyle, eating habits, and perceived stress [2]. The CDC states that about 78.6 million American adults are overweight or obese [3], and about 25% to 40% of US adults attempt to lose weight each year [4]. It is unclear what proportion of those who lose weight are successful at maintaining their weight loss [5,6].

Researchers and practitioners alike understand that maintaining weight loss is difficult. Most studies on weight regain have focused on biological and lifestyle factors [7]. This article did a good job in detailing gaps in knowledge and supporting the need for further study of psychosocial variables. The results demonstrated complex, interactive relationships between multiple factors. Three psychosocial variables were found to be statistically significant in relation to weight loss, but more significant relationships were found between race, perceived stress, and weight loss.

As a secondary analysis, this study carries the strengths of the initial study, including a 30-month study duration. In addition, this study was randomized and included 3 different treatment arms. Lastly, there was a large representation of black participants, and the authors suggested that the results may offer and initial characterization of this population.

A limitation of this study was the use of self-reported data, which may be subject to be bias and be less reliable than direct measured data. Also, these measures were apparently taken only once prior to the beginning of phase 2 with no rationale provided. Psychosocial variables such as social support, quality of life, and perceived stress are dynamic and cannot be accurately encapsulated in isolated moments in time. The sample’s diversity was also lacking as there were few Hispanics and no Asian
participants.

Applications for Clinical Practice

A few significant, interactive relationships were discovered in this examination and require further study. Continued research and a better understanding of these complex relationships are needed.

—Angela M. Godwin Beoku-Betts, MSN, FNP–BC

Study Overview

Objective. To identify psychosocial predictors of weight loss maintenance in a multi-site clinical trial following a group-based weight loss program.

Design. Secondary analysis of a 2-phase randomized controlled trial. The first phase was a 6-month group-based weight loss program and the second phase was a 30-month trial comparing weight loss maintenance strategies.

Setting and participants. The patients studied were participants in the Weight Loss Maintenance trial [1], which was conducted at 4 US clinical centers. Eligible participants were overweight and obese adults with a BMI between 25 to 45 who were actively taking medication for hypertension, dyslipidemia, or both. 1685 patients were recruited into the weight-loss phase, and those who lost at least 4 kg (n = 1032) were then randomly assigned to 1 of the 3 maintenance arms: (a) self-directed with minimal intervention (control), (b) interactive technology that consisted of unlimited, interactive study website access, or (c) personal contact consisting of monthly, personalized telephone calls and quarterly face-to-face contact with a study interventionist. There was 1 death in each treatment group, so a total of 1029 participants were included in the analyses.

Main outcome measures. The researchers examined the associations between psychosocial variables and weight change outcomes at 12 and 30 months. Patients completed 5 self-report measures at the time of randomization into phase 2 of the study: a social support and exercise survey, a social support and eating habits survey, the SF-36, the Patient Health Questionnaire Depression Scale, and the Perceived Stress Scale.

Results. Of the 1029 participants initially included for analyses, 2 failed to provide complete data on the social support scales and 2 were identified as outliers at both 12 and 30 months. This resulted in a final sample size of 1025 participants; 63% were women, 61% were non-Latino white, and 38% were black. The mean age was 55.6 years. All groups regained weight, with the personal contact group having the least amount of gain. However, the mean weight at 30 months remained significantly lower than the mean weight at entry into phase 1.

Only 3 psychosocial variables were significantly related to weight loss at 12 and 30 months. At both time marks, less weight regain was associated with higher SF-36 mental health composite scores (P < 0.01). Interestingly, for black participants at the 12-month mark, more weight regain was associated with higher exercise encouragement from friends (P < 0.05). At 30 months, more weight regain was associated with friends’ encouragement for healthy eating (P < 0.05).

Conclusion. The psychosocial variables that were self-reported upon entering phase 2 may predict the ability of an individual to maintain weight loss at 12 and 30 months. The significant, complex interactions between these variables, race, sex, and treatment interventions need to be further studied for proper incorporation into a weight loss maintenance program.

Commentary

The case for the obesity epidemic has been long established. Unfortunately, the causes for weight gain can be complicated and multifactorial. Factors associated with weight gain include nonmodifiable factors such as age, sex, and race as well as modifiable factors like lifestyle, eating habits, and perceived stress [2]. The CDC states that about 78.6 million American adults are overweight or obese [3], and about 25% to 40% of US adults attempt to lose weight each year [4]. It is unclear what proportion of those who lose weight are successful at maintaining their weight loss [5,6].

Researchers and practitioners alike understand that maintaining weight loss is difficult. Most studies on weight regain have focused on biological and lifestyle factors [7]. This article did a good job in detailing gaps in knowledge and supporting the need for further study of psychosocial variables. The results demonstrated complex, interactive relationships between multiple factors. Three psychosocial variables were found to be statistically significant in relation to weight loss, but more significant relationships were found between race, perceived stress, and weight loss.

As a secondary analysis, this study carries the strengths of the initial study, including a 30-month study duration. In addition, this study was randomized and included 3 different treatment arms. Lastly, there was a large representation of black participants, and the authors suggested that the results may offer and initial characterization of this population.

A limitation of this study was the use of self-reported data, which may be subject to be bias and be less reliable than direct measured data. Also, these measures were apparently taken only once prior to the beginning of phase 2 with no rationale provided. Psychosocial variables such as social support, quality of life, and perceived stress are dynamic and cannot be accurately encapsulated in isolated moments in time. The sample’s diversity was also lacking as there were few Hispanics and no Asian
participants.

Applications for Clinical Practice

A few significant, interactive relationships were discovered in this examination and require further study. Continued research and a better understanding of these complex relationships are needed.

—Angela M. Godwin Beoku-Betts, MSN, FNP–BC

Study Overview

Objective. To determine the association between bariatric surgery and long-term mortality rates among patients with severe obesity.

Design. Retrospective cohort study.

Setting and participants. This analysis relied upon data from Veteran’s Administration (VA) patients undergoing bariatric surgery between 2000 and 2011 and a group of matched controls. For this data-only study, a waiver of informed consent was obtained. Investigators first used the VA Surgical Quality Improvement Program (SQIP) dataset to identify all bariatric surgical procedures performed at VA hospitals between 2000 and the end of 2011, excluding patients who had any evidence of body mass index (BMI) less than 35 kg/m² and those with certain baseline diagnoses that would be considered contraindications for surgery, as well as those who had prolonged inpatient stays immediately prior to their surgical date. No upper or lower age limits appear to have been specified, and no upper BMI limit appeared to have been set.

Once all surgical patients were identified, the investigators attempted to find a group of similar control patients who had not undergone surgery. Initially they pulled candidate matches for each surgical patient based on having the same sex, age-group (within 5 years), BMI category (35-40, 40-50, >50), diabetes status (present or absent), racial category, and VA region. From these candidates, they selected up to 3 of the closest matches on age, BMI, and a composite comorbidity score based on inpatient and outpatient claims in the year prior to surgery. The authors specified that controls could convert to surgical patients during the follow-up period, in which case their data was censored beginning with the surgical procedure. However, if a control patient underwent surgery during 2012 or 2013, censoring was not possible given that the dataset for identifying surgeries contained only procedures performed through the end of 2011.

Main outcome measures. The primary outcome of interest was time to death (any cause) beginning at the date of surgery (or baseline date for nonsurgical controls) through the end of 2013. The investigators built Cox proportional hazards models to evaluate survival using multivariable models to adjust for baseline characteristics, including those involved in the matching process, as well as others that might have differentially impacted both likelihood of undergoing surgery and mortality risk. These included marital status, insurance markers of low income or disability, and a number of comorbid medical and psychiatric diagnoses.

In addition to the main analyses, the investigators also looked for effect modification of the surgery-mortality relationship by a patient’s sex and presence or absence of diabetes at the time of surgery, as well as the time period in which their surgery was conducted, dichotomized around the year 2006. This year was selected for several reasons, including that it was the year in which a VA-wide comprehensive weight management and surgical selection program was instituted.

Results. The surgical cohort was made up of 2500 patients, and there were 7462 matched controls. The surgical and control groups were similar with respect to matched baseline characteristics, tested using standardized differences (as opposed to t test or chi-square). Mean (SD) age was 52 (8.8) years for surgical patients versus 53 (8.7) years for controls. 74% of patients in both the surgical and control groups were men, and 81% in both groups were white (ethnicity not specified). Mean (SD) baseline BMI was 47 (7.9) kg/m² in the surgical group and 46 (7.3) kg/m² for controls.

Some between-group differences were present for baseline characteristics that had not been included in the matching protocol. More surgical patients than controls had diagnoses of hypertension (80% surgical vs. 70% control), dyslipidemia (61% vs. 52%), arthritis (27% vs. 15%), depression (44% vs. 32%), GERD (35% vs.19%), and fatty liver disease (6.6% vs. 0.6%). In contrast, more control patients than surgical patients had diagnoses of alcohol abuse (6.2% in controls vs. 3.9% in surgical) and schizophrenia (4.9% vs. 1.8%). Also, although a number of different surgical types were represented in the cohort, the vast majority of procedures were classified as Roux-en-Y gastric bypasses (RYGB). 53% of the procedures were open RYGB, 21% were laparoscopic RYGB, 10% were adjustable gastric bands (AGB), and 15% were vertical sleeve gastrectomies (VSG).

Mortality was lower among surgical patients than among matched controls during a mean follow-up time of 6.9 years for surgical patients and 6.6 years for controls. Namely, the 1-, 5- and 10-year cumulative mortality rates for surgical patients were: 2.4%, 6.4%, and 13.8%. Unadjusted mortality rates for nonsurgical controls were lower initially (1.7% at 1 year), but then much higher at years 5 (10.4%), and 10 (23.9%). In multivariable Cox models, the hazard ratio (HR) for mortality in bariatric patients versus controls was nonsignificant at 1 year of follow-up. However, between 1 and 5 years after surgery (or after baseline), multivariable models showed an HR (95% CI) of 0.45 (0.36–0.56) for mortality among surgical patients versus controls. For those with more than 5 years of follow up, the HR was similar (0.47, 95% CI 0.39–0.58) for death among surgical versus control patients. The investigators found that the year during which a patient underwent surgery (before or after 2006) did impact mortality during the first postoperative year, with those who had earlier procedures (2000-2005) exhibiting a significantly higher risk of death in that year relative to non-operative controls (HR 1.66, 95% CI 1.19–2.33). No significant sex or diabetes interactions were observed for the surgery-mortality relationship in multivariable Cox models. There was no information provided as to the breakdown of cause of death within the larger “all-cause mortality” outcome.

Conclusion. Bariatric surgery was associated with significantly lower all-cause mortality among surgical patients in the VA over a 5- to 14-year follow-up period compared with a group of severely obese VA patients who did not undergo surgery.

Commentary

Rates of severe obesity (BMI ≥ 35 kg/m²) have risen at a faster pace than those of obesity in the United States over the past decade [1], driving clinicians, patients and payers to search for effective methods of treating this condition. Bariatric surgery has emerged as the most effective treatment for severe obesity; however, the existing surgical literature is predominated by studies with short- or medium-term postoperative follow-up and homogenous participant populations containing large numbers of younger non-Hispanic white women. Research from the Swedish Obesity Study (SOS), as well as smaller US-based studies, has suggested that severely obese patients who undergo bariatric surgery have better long-term survival than their nonsurgical counterparts [2,3].Counter to this finding, a previous medium-term study utilizing data from VA hospitals did not find that surgery conferred a mortality benefit among this largely male, older, and sicker patient population [4].The current study, by the same group of investigators, attempts to update the previous finding by including more recent surgical data and a longer follow-up period, to see whether or not a survival benefit appears to emerge for VA patients undergoing bariatric surgery.

A major strength of this study was the use of a large and comprehensive clinical dataset, a strength of many studies utilizing data from the VA. The availability of clinical data such as BMI, as well as diagnostic codes and sociodemographic variables, allowed the authors to match and adjust for a number of potential confounders of the surgery-mortality relationship. Another unique feature of VA data is that members of this health care system can often be followed regardless of their location, as the unified medical record transfers between states. This is in contrast to many claims-based or single-center studies of surgery, where patients are lost to follow-up if they move or transfer insurance providers. This study clearly benefited from this aspect of VA data, with a mean postoperative follow-up period of over 5 years in both study groups, much longer than is typically observed in bariatric surgical studies, and probably a necessary feature for examining more of a rare outcome such as mortality (as opposed to comparing weight loss or diabetes remission). Another clear contribution of this study is that it focused on a group of patients not typical of bariatric cohorts—this group was slightly older and sicker, with far more men than women, and therefore at a much higher risk of mortality than the typically younger females that are part of most studies.

Although the authors did adjust for many factors when comparing the surgical and nonsurgical groups, it is possible, as with any observational study, that unmeasured confounders may have been present. Psychosocial and behavioral features that may be linked both to a person’s likelihood of undergoing surgery, and to their mortality risk are of particular concern. It is worth noting, for example, that far more patients in the nonsurgical group were identified as schizophrenic, and that the rate of schizophrenia in that severely obese group was much higher than that of the general population. This pattern may have some relationship to the weight-gain promoting effect of antipsychotic medications and the unfortunate reality that patients with severe obesity and severe mental illness may not be as well equipped to seek out surgery (or viewed as acceptable candidates) as those without severe mental illness. One possible limitation mentioned by the authors was that control group patients who underwent surgery in 2012 or 2013 would not have been recognized (and thus had their data censored in this study), possibly leading to incorrect categorization of exposure category for some amount of person-time during follow-up. In general, though, there is a low likelihood of this phenomenon impacting the findings, given both the relative infrequency of crossover observed in the cohort prior to 2011, and the relatively short amount of person-time any later crossovers would have contributed in the later years of the study.

Although codes for baseline disease states were adjusted for in multivariable analyses, the surgical patients were in general a medically sicker group at baseline than control patients. As the authors point out, if anything, this should have biased the findings in favor of seeing higher mortality rate in the surgical group, the opposite of what was found. Further strengthening the finding of a correlation between survival and surgery is the mix of procedure types included in this study. Over half of the procedures were open RYGB surgeries, with far fewer of the more modern and lower risk procedures (eg, laparoscopic RYGB) represented. Again, this mix of procedures would be expected to result in an overestimation of mortality in surgical patients relative to what might be observed if all patients had been drawn from later years of the cohort, as surgical technique evolved.

Applications for Clinical Practice

This study adds to the evidence that patients with severe obesity who undergo bariatric surgery have a lower risk of death up to 10 years after their surgery compared with patients who do not have these procedures. The findings of this work should provide encouragement, particularly for managaing older adults with more longstanding comorbidities. Those who are strongly motivated to pursue weight loss surgery, and who are deemed good candidates by bariatric teams, may add years to their lives by undergoing one of these procedures. As always, however, the quality of life experienced by patients after surgery, and a realistic expectation of the ways in which surgery will fundamentally change their lifestyle, must be a critical part of the discussion.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine the association between bariatric surgery and long-term mortality rates among patients with severe obesity.

Design. Retrospective cohort study.

Setting and participants. This analysis relied upon data from Veteran’s Administration (VA) patients undergoing bariatric surgery between 2000 and 2011 and a group of matched controls. For this data-only study, a waiver of informed consent was obtained. Investigators first used the VA Surgical Quality Improvement Program (SQIP) dataset to identify all bariatric surgical procedures performed at VA hospitals between 2000 and the end of 2011, excluding patients who had any evidence of body mass index (BMI) less than 35 kg/m² and those with certain baseline diagnoses that would be considered contraindications for surgery, as well as those who had prolonged inpatient stays immediately prior to their surgical date. No upper or lower age limits appear to have been specified, and no upper BMI limit appeared to have been set.

Once all surgical patients were identified, the investigators attempted to find a group of similar control patients who had not undergone surgery. Initially they pulled candidate matches for each surgical patient based on having the same sex, age-group (within 5 years), BMI category (35-40, 40-50, >50), diabetes status (present or absent), racial category, and VA region. From these candidates, they selected up to 3 of the closest matches on age, BMI, and a composite comorbidity score based on inpatient and outpatient claims in the year prior to surgery. The authors specified that controls could convert to surgical patients during the follow-up period, in which case their data was censored beginning with the surgical procedure. However, if a control patient underwent surgery during 2012 or 2013, censoring was not possible given that the dataset for identifying surgeries contained only procedures performed through the end of 2011.

Main outcome measures. The primary outcome of interest was time to death (any cause) beginning at the date of surgery (or baseline date for nonsurgical controls) through the end of 2013. The investigators built Cox proportional hazards models to evaluate survival using multivariable models to adjust for baseline characteristics, including those involved in the matching process, as well as others that might have differentially impacted both likelihood of undergoing surgery and mortality risk. These included marital status, insurance markers of low income or disability, and a number of comorbid medical and psychiatric diagnoses.

In addition to the main analyses, the investigators also looked for effect modification of the surgery-mortality relationship by a patient’s sex and presence or absence of diabetes at the time of surgery, as well as the time period in which their surgery was conducted, dichotomized around the year 2006. This year was selected for several reasons, including that it was the year in which a VA-wide comprehensive weight management and surgical selection program was instituted.

Results. The surgical cohort was made up of 2500 patients, and there were 7462 matched controls. The surgical and control groups were similar with respect to matched baseline characteristics, tested using standardized differences (as opposed to t test or chi-square). Mean (SD) age was 52 (8.8) years for surgical patients versus 53 (8.7) years for controls. 74% of patients in both the surgical and control groups were men, and 81% in both groups were white (ethnicity not specified). Mean (SD) baseline BMI was 47 (7.9) kg/m² in the surgical group and 46 (7.3) kg/m² for controls.

Some between-group differences were present for baseline characteristics that had not been included in the matching protocol. More surgical patients than controls had diagnoses of hypertension (80% surgical vs. 70% control), dyslipidemia (61% vs. 52%), arthritis (27% vs. 15%), depression (44% vs. 32%), GERD (35% vs.19%), and fatty liver disease (6.6% vs. 0.6%). In contrast, more control patients than surgical patients had diagnoses of alcohol abuse (6.2% in controls vs. 3.9% in surgical) and schizophrenia (4.9% vs. 1.8%). Also, although a number of different surgical types were represented in the cohort, the vast majority of procedures were classified as Roux-en-Y gastric bypasses (RYGB). 53% of the procedures were open RYGB, 21% were laparoscopic RYGB, 10% were adjustable gastric bands (AGB), and 15% were vertical sleeve gastrectomies (VSG).

Mortality was lower among surgical patients than among matched controls during a mean follow-up time of 6.9 years for surgical patients and 6.6 years for controls. Namely, the 1-, 5- and 10-year cumulative mortality rates for surgical patients were: 2.4%, 6.4%, and 13.8%. Unadjusted mortality rates for nonsurgical controls were lower initially (1.7% at 1 year), but then much higher at years 5 (10.4%), and 10 (23.9%). In multivariable Cox models, the hazard ratio (HR) for mortality in bariatric patients versus controls was nonsignificant at 1 year of follow-up. However, between 1 and 5 years after surgery (or after baseline), multivariable models showed an HR (95% CI) of 0.45 (0.36–0.56) for mortality among surgical patients versus controls. For those with more than 5 years of follow up, the HR was similar (0.47, 95% CI 0.39–0.58) for death among surgical versus control patients. The investigators found that the year during which a patient underwent surgery (before or after 2006) did impact mortality during the first postoperative year, with those who had earlier procedures (2000-2005) exhibiting a significantly higher risk of death in that year relative to non-operative controls (HR 1.66, 95% CI 1.19–2.33). No significant sex or diabetes interactions were observed for the surgery-mortality relationship in multivariable Cox models. There was no information provided as to the breakdown of cause of death within the larger “all-cause mortality” outcome.

Conclusion. Bariatric surgery was associated with significantly lower all-cause mortality among surgical patients in the VA over a 5- to 14-year follow-up period compared with a group of severely obese VA patients who did not undergo surgery.

Commentary

Rates of severe obesity (BMI ≥ 35 kg/m²) have risen at a faster pace than those of obesity in the United States over the past decade [1], driving clinicians, patients and payers to search for effective methods of treating this condition. Bariatric surgery has emerged as the most effective treatment for severe obesity; however, the existing surgical literature is predominated by studies with short- or medium-term postoperative follow-up and homogenous participant populations containing large numbers of younger non-Hispanic white women. Research from the Swedish Obesity Study (SOS), as well as smaller US-based studies, has suggested that severely obese patients who undergo bariatric surgery have better long-term survival than their nonsurgical counterparts [2,3].Counter to this finding, a previous medium-term study utilizing data from VA hospitals did not find that surgery conferred a mortality benefit among this largely male, older, and sicker patient population [4].The current study, by the same group of investigators, attempts to update the previous finding by including more recent surgical data and a longer follow-up period, to see whether or not a survival benefit appears to emerge for VA patients undergoing bariatric surgery.

A major strength of this study was the use of a large and comprehensive clinical dataset, a strength of many studies utilizing data from the VA. The availability of clinical data such as BMI, as well as diagnostic codes and sociodemographic variables, allowed the authors to match and adjust for a number of potential confounders of the surgery-mortality relationship. Another unique feature of VA data is that members of this health care system can often be followed regardless of their location, as the unified medical record transfers between states. This is in contrast to many claims-based or single-center studies of surgery, where patients are lost to follow-up if they move or transfer insurance providers. This study clearly benefited from this aspect of VA data, with a mean postoperative follow-up period of over 5 years in both study groups, much longer than is typically observed in bariatric surgical studies, and probably a necessary feature for examining more of a rare outcome such as mortality (as opposed to comparing weight loss or diabetes remission). Another clear contribution of this study is that it focused on a group of patients not typical of bariatric cohorts—this group was slightly older and sicker, with far more men than women, and therefore at a much higher risk of mortality than the typically younger females that are part of most studies.

Although the authors did adjust for many factors when comparing the surgical and nonsurgical groups, it is possible, as with any observational study, that unmeasured confounders may have been present. Psychosocial and behavioral features that may be linked both to a person’s likelihood of undergoing surgery, and to their mortality risk are of particular concern. It is worth noting, for example, that far more patients in the nonsurgical group were identified as schizophrenic, and that the rate of schizophrenia in that severely obese group was much higher than that of the general population. This pattern may have some relationship to the weight-gain promoting effect of antipsychotic medications and the unfortunate reality that patients with severe obesity and severe mental illness may not be as well equipped to seek out surgery (or viewed as acceptable candidates) as those without severe mental illness. One possible limitation mentioned by the authors was that control group patients who underwent surgery in 2012 or 2013 would not have been recognized (and thus had their data censored in this study), possibly leading to incorrect categorization of exposure category for some amount of person-time during follow-up. In general, though, there is a low likelihood of this phenomenon impacting the findings, given both the relative infrequency of crossover observed in the cohort prior to 2011, and the relatively short amount of person-time any later crossovers would have contributed in the later years of the study.

Although codes for baseline disease states were adjusted for in multivariable analyses, the surgical patients were in general a medically sicker group at baseline than control patients. As the authors point out, if anything, this should have biased the findings in favor of seeing higher mortality rate in the surgical group, the opposite of what was found. Further strengthening the finding of a correlation between survival and surgery is the mix of procedure types included in this study. Over half of the procedures were open RYGB surgeries, with far fewer of the more modern and lower risk procedures (eg, laparoscopic RYGB) represented. Again, this mix of procedures would be expected to result in an overestimation of mortality in surgical patients relative to what might be observed if all patients had been drawn from later years of the cohort, as surgical technique evolved.

Applications for Clinical Practice

This study adds to the evidence that patients with severe obesity who undergo bariatric surgery have a lower risk of death up to 10 years after their surgery compared with patients who do not have these procedures. The findings of this work should provide encouragement, particularly for managaing older adults with more longstanding comorbidities. Those who are strongly motivated to pursue weight loss surgery, and who are deemed good candidates by bariatric teams, may add years to their lives by undergoing one of these procedures. As always, however, the quality of life experienced by patients after surgery, and a realistic expectation of the ways in which surgery will fundamentally change their lifestyle, must be a critical part of the discussion.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine the association between bariatric surgery and long-term mortality rates among patients with severe obesity.

Design. Retrospective cohort study.

Setting and participants. This analysis relied upon data from Veteran’s Administration (VA) patients undergoing bariatric surgery between 2000 and 2011 and a group of matched controls. For this data-only study, a waiver of informed consent was obtained. Investigators first used the VA Surgical Quality Improvement Program (SQIP) dataset to identify all bariatric surgical procedures performed at VA hospitals between 2000 and the end of 2011, excluding patients who had any evidence of body mass index (BMI) less than 35 kg/m² and those with certain baseline diagnoses that would be considered contraindications for surgery, as well as those who had prolonged inpatient stays immediately prior to their surgical date. No upper or lower age limits appear to have been specified, and no upper BMI limit appeared to have been set.

Once all surgical patients were identified, the investigators attempted to find a group of similar control patients who had not undergone surgery. Initially they pulled candidate matches for each surgical patient based on having the same sex, age-group (within 5 years), BMI category (35-40, 40-50, >50), diabetes status (present or absent), racial category, and VA region. From these candidates, they selected up to 3 of the closest matches on age, BMI, and a composite comorbidity score based on inpatient and outpatient claims in the year prior to surgery. The authors specified that controls could convert to surgical patients during the follow-up period, in which case their data was censored beginning with the surgical procedure. However, if a control patient underwent surgery during 2012 or 2013, censoring was not possible given that the dataset for identifying surgeries contained only procedures performed through the end of 2011.

Main outcome measures. The primary outcome of interest was time to death (any cause) beginning at the date of surgery (or baseline date for nonsurgical controls) through the end of 2013. The investigators built Cox proportional hazards models to evaluate survival using multivariable models to adjust for baseline characteristics, including those involved in the matching process, as well as others that might have differentially impacted both likelihood of undergoing surgery and mortality risk. These included marital status, insurance markers of low income or disability, and a number of comorbid medical and psychiatric diagnoses.

In addition to the main analyses, the investigators also looked for effect modification of the surgery-mortality relationship by a patient’s sex and presence or absence of diabetes at the time of surgery, as well as the time period in which their surgery was conducted, dichotomized around the year 2006. This year was selected for several reasons, including that it was the year in which a VA-wide comprehensive weight management and surgical selection program was instituted.

Results. The surgical cohort was made up of 2500 patients, and there were 7462 matched controls. The surgical and control groups were similar with respect to matched baseline characteristics, tested using standardized differences (as opposed to t test or chi-square). Mean (SD) age was 52 (8.8) years for surgical patients versus 53 (8.7) years for controls. 74% of patients in both the surgical and control groups were men, and 81% in both groups were white (ethnicity not specified). Mean (SD) baseline BMI was 47 (7.9) kg/m² in the surgical group and 46 (7.3) kg/m² for controls.

Some between-group differences were present for baseline characteristics that had not been included in the matching protocol. More surgical patients than controls had diagnoses of hypertension (80% surgical vs. 70% control), dyslipidemia (61% vs. 52%), arthritis (27% vs. 15%), depression (44% vs. 32%), GERD (35% vs.19%), and fatty liver disease (6.6% vs. 0.6%). In contrast, more control patients than surgical patients had diagnoses of alcohol abuse (6.2% in controls vs. 3.9% in surgical) and schizophrenia (4.9% vs. 1.8%). Also, although a number of different surgical types were represented in the cohort, the vast majority of procedures were classified as Roux-en-Y gastric bypasses (RYGB). 53% of the procedures were open RYGB, 21% were laparoscopic RYGB, 10% were adjustable gastric bands (AGB), and 15% were vertical sleeve gastrectomies (VSG).

Mortality was lower among surgical patients than among matched controls during a mean follow-up time of 6.9 years for surgical patients and 6.6 years for controls. Namely, the 1-, 5- and 10-year cumulative mortality rates for surgical patients were: 2.4%, 6.4%, and 13.8%. Unadjusted mortality rates for nonsurgical controls were lower initially (1.7% at 1 year), but then much higher at years 5 (10.4%), and 10 (23.9%). In multivariable Cox models, the hazard ratio (HR) for mortality in bariatric patients versus controls was nonsignificant at 1 year of follow-up. However, between 1 and 5 years after surgery (or after baseline), multivariable models showed an HR (95% CI) of 0.45 (0.36–0.56) for mortality among surgical patients versus controls. For those with more than 5 years of follow up, the HR was similar (0.47, 95% CI 0.39–0.58) for death among surgical versus control patients. The investigators found that the year during which a patient underwent surgery (before or after 2006) did impact mortality during the first postoperative year, with those who had earlier procedures (2000-2005) exhibiting a significantly higher risk of death in that year relative to non-operative controls (HR 1.66, 95% CI 1.19–2.33). No significant sex or diabetes interactions were observed for the surgery-mortality relationship in multivariable Cox models. There was no information provided as to the breakdown of cause of death within the larger “all-cause mortality” outcome.

Conclusion. Bariatric surgery was associated with significantly lower all-cause mortality among surgical patients in the VA over a 5- to 14-year follow-up period compared with a group of severely obese VA patients who did not undergo surgery.

Commentary

Rates of severe obesity (BMI ≥ 35 kg/m²) have risen at a faster pace than those of obesity in the United States over the past decade [1], driving clinicians, patients and payers to search for effective methods of treating this condition. Bariatric surgery has emerged as the most effective treatment for severe obesity; however, the existing surgical literature is predominated by studies with short- or medium-term postoperative follow-up and homogenous participant populations containing large numbers of younger non-Hispanic white women. Research from the Swedish Obesity Study (SOS), as well as smaller US-based studies, has suggested that severely obese patients who undergo bariatric surgery have better long-term survival than their nonsurgical counterparts [2,3].Counter to this finding, a previous medium-term study utilizing data from VA hospitals did not find that surgery conferred a mortality benefit among this largely male, older, and sicker patient population [4].The current study, by the same group of investigators, attempts to update the previous finding by including more recent surgical data and a longer follow-up period, to see whether or not a survival benefit appears to emerge for VA patients undergoing bariatric surgery.

A major strength of this study was the use of a large and comprehensive clinical dataset, a strength of many studies utilizing data from the VA. The availability of clinical data such as BMI, as well as diagnostic codes and sociodemographic variables, allowed the authors to match and adjust for a number of potential confounders of the surgery-mortality relationship. Another unique feature of VA data is that members of this health care system can often be followed regardless of their location, as the unified medical record transfers between states. This is in contrast to many claims-based or single-center studies of surgery, where patients are lost to follow-up if they move or transfer insurance providers. This study clearly benefited from this aspect of VA data, with a mean postoperative follow-up period of over 5 years in both study groups, much longer than is typically observed in bariatric surgical studies, and probably a necessary feature for examining more of a rare outcome such as mortality (as opposed to comparing weight loss or diabetes remission). Another clear contribution of this study is that it focused on a group of patients not typical of bariatric cohorts—this group was slightly older and sicker, with far more men than women, and therefore at a much higher risk of mortality than the typically younger females that are part of most studies.

Although the authors did adjust for many factors when comparing the surgical and nonsurgical groups, it is possible, as with any observational study, that unmeasured confounders may have been present. Psychosocial and behavioral features that may be linked both to a person’s likelihood of undergoing surgery, and to their mortality risk are of particular concern. It is worth noting, for example, that far more patients in the nonsurgical group were identified as schizophrenic, and that the rate of schizophrenia in that severely obese group was much higher than that of the general population. This pattern may have some relationship to the weight-gain promoting effect of antipsychotic medications and the unfortunate reality that patients with severe obesity and severe mental illness may not be as well equipped to seek out surgery (or viewed as acceptable candidates) as those without severe mental illness. One possible limitation mentioned by the authors was that control group patients who underwent surgery in 2012 or 2013 would not have been recognized (and thus had their data censored in this study), possibly leading to incorrect categorization of exposure category for some amount of person-time during follow-up. In general, though, there is a low likelihood of this phenomenon impacting the findings, given both the relative infrequency of crossover observed in the cohort prior to 2011, and the relatively short amount of person-time any later crossovers would have contributed in the later years of the study.

Although codes for baseline disease states were adjusted for in multivariable analyses, the surgical patients were in general a medically sicker group at baseline than control patients. As the authors point out, if anything, this should have biased the findings in favor of seeing higher mortality rate in the surgical group, the opposite of what was found. Further strengthening the finding of a correlation between survival and surgery is the mix of procedure types included in this study. Over half of the procedures were open RYGB surgeries, with far fewer of the more modern and lower risk procedures (eg, laparoscopic RYGB) represented. Again, this mix of procedures would be expected to result in an overestimation of mortality in surgical patients relative to what might be observed if all patients had been drawn from later years of the cohort, as surgical technique evolved.

Applications for Clinical Practice

This study adds to the evidence that patients with severe obesity who undergo bariatric surgery have a lower risk of death up to 10 years after their surgery compared with patients who do not have these procedures. The findings of this work should provide encouragement, particularly for managaing older adults with more longstanding comorbidities. Those who are strongly motivated to pursue weight loss surgery, and who are deemed good candidates by bariatric teams, may add years to their lives by undergoing one of these procedures. As always, however, the quality of life experienced by patients after surgery, and a realistic expectation of the ways in which surgery will fundamentally change their lifestyle, must be a critical part of the discussion.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine the efficacy, both short and long term, of a behavioral intervention targeting overweight parents and their children simultaneously versus an intervention focused on weight management only for the child within the patient-centered medical home (PCMH).

Design. 4-center, 2-arm, randomized controlled trial.

Setting and participants. Study participants were recruited from 4 urban/suburban pediatric practices. Primary care providers (PCPs) recruited patients at the time of well or sick visits based on body mass index (BMI) flagged prior to the visit by Patient Enhancement Assistants (PEAs). 171 parent/child dyads were assessed for eligibility and 105 were randomized in blocks of 12 dyads using a random number generator and stratified by child’s gender. Pediatricians were blind to their patient’s group assignments. Inclusion criteria were as follows: children aged 2–5 with a BMI higher than the 85th percentile for both age and gender, and 1 parent with a BMI greater than 25. Exclusion criteria were limited to children who were small for gestational age and/or short stature, and child or parent inability to perform physical activity. Specific precautions were taken to prevent contamination between intervention and information control (IC) groups [1].

Intervention. Three PEAs who held a masters or bachelors degree in psychology, nutrition, exercise science, or equivalent, or were registered dietitians, were embedded within each PCMH practice. For both the intervention and IC groups, parents attended 13 one-hour group sessions led by a PEA over a 12-month period, followed by a 12-month follow-up period with 3 additional meetings. The PEA telephoned parents between scheduled meetings. Pediatricians reviewed child’s weight changes every 6 months during scheduled appointments and the PEA sent progress notes in between these visits [2]. Dietary, physical, and sedentary activity guidelines were given based on the recommendations of a national multi-organizational expert committee [3]. Parents were given specific goals for their child, including a 0.5- to 1-pound per week loss, 60 minutes per day of physical activity, and limiting TV and screen time to less than 2 hours per day.

In addition, the intervention group received parenting and behavior change strategies to promote both parent and child weight loss. Parents were instructed to weigh themselves and their child once per week and monitor physical activity and diet. They received individual meetings with the PEA before or after group meetings to review goal setting and food/physical activity diaries. Parents were also given a weight loss goal of 1 to 2 pounds per week and were advised to model physical activity by engaging in active play with their child for at least 10 minutes per day.

Main outcome measures. The main outcome measures were %0BMI and z-BMI. Percent 0BMI is defined as [(child’s BMI – 50th percentile BMI)/50th percentile BMI] x 100 [2]. The authors chose %0BMI as the primary outcome measure because z-BMI can diminish the effect of weight change in heavier children [4]. Both measures were expressed as mean ± standard error (SEM). Parent weight change was measured using BMI alone.

The child’s weight was measured at each session and height was measured at baseline, 3, 6, 12, 18, and 24 months. Parent weight was measured every session in the intervention group, but only at baseline, 6, 12, 18, and 24 months in the IC group. A standardized protocol was followed for all height and weight measurements. An intention to treat analysis (ITT) was conducted on all parent/child dyads, regardless of whether or not they completed the study (n = 96).

Results. Research assistants assessed 171 parent/child dyads for eligibility. 66 were excluded for either not meeting inclusion criteria (n = 24) or declining to participate (n = 42). 105 dyads were randomized, but 9 did not receive the allocated intervention because they did not start the study, resulting in a total of 96 dyads included in analysis: 46 in the intervention group and 50 in the IC. Twelve- and 24-month completion rates were 83% and 73% respectively; there was no difference in attrition between intervention and IC groups.

The mean child ages of the intervention and IC groups were 4.6 ± 0.2 and 4.4 ± 0.2 years, respectively. 33 of the 46 children in the intervention group and 37 of the 50 children in the IC group were identified as non-Hispanic white. The mean yearly income of all families was $65,729 ± $3068, with only 8.3% of families below $20,000.

The intervention group had greater decreases in child %0BMI from baseline to 6, 12, 18, and 24 months than the IC group. Similar trends were seen with child z-BMI. A slower increase in height was observed in the intervention group when compared with the IC at both 18 months (P < 0.001) and at 24 months (P < 0.02). Parents showed greater overall BMI reduction in the intervention group as opposed to the IC group at all time points (P < 0.001). BMI changes achieved at 6 months were maintained at 24 months. %0BMI and parent BMI changes were correlated from baseline to 12, 18, and 24 months. No significant baseline moderators were found among the children in either group.

Conclusion. This study demonstrated that within the PCMH model of pediatric primary care, an intervention focused on joint behavior change and weight modification treatment of parents and children led to better initial and sustained improvements in %0BMI and z-BMI (in children) and BMI (in parents) than a child-focused IC.

Commentary

Over one-third of children and adolescents are considered to be overweight or have obesity, a number that has doubled in the past 30 years [5]. Pediatrician and primary care physician visits are optimal places to identify overweight children who are at risk for obesity and begin prevention measures, although identifying overweight and obese younger children can be difficult [6]. This study used PEAs to aid physicians in identification, implementation, and delivery. With increasing evidence to support pediatrician involvement in intensive weight management in a primary versus specialty care setting, embedding PEAs within the PCMH model may be an important way to help deliver care for overweight/obese children [7].

Although many approaches have been considered to target childhood obesity, this study represents an important contribution to the literature because it demonstrates that a primary care–based intervention targeting parents as well as their young children is more efficacious for weight management than a more traditional, child-only focused intervention. In addition, the intervention included many different evidence-based components such as teaching behavior modification techniques to parents, consideration of parenting styles and techniques, and encouraging simultaneous parental weight modification. While the U.S. Preventive Services Task Force (USPSTF) recommends intensive interventions with 30 sessions over 2 years [8], this study was able to accomplish significant weight change in 13 sessions.

This intervention is unique in its integration of parenting techniques with other evidence-based strategies for child weight management. Although it has been shown in the literature that certain parenting styles can positively impact children’s health behaviors [9], namely the use of positive reinforcement and monitoring children’s health practices [10], only a few studies have looked at the impact of parenting interventions on childhood obesity. Mazzeo et al demonstrated a significant reduction in child BMI with a parenting-only intervention in the NOURISH trial [11], Slusser et al found a significant child BMI reduction using parent training for low-income, 2- to 4-year-old children [12], and Brotman et al conducted a longitudinal study demonstrating that a family intervention could decrease BMI and improve overall child health behaviors [13]. Despite these aforementioned studies, there is a lack of longitudinal data on the association between general parenting style and weight [14], and this study addresses this gap in literature by providing 2-year follow-up and demonstrating sustained impact on the intervention group.

This study had many additional strengths, including randomized design, primary care physician blinding, use of intention to treat analysis, standardization of measurement tools, clear justification of sample size, long-term follow-up, and the use of child-appropriate BMI measures (eg, %0BMI vs. z-BMI as primary outcome measure). In addition, the intervention setting in a PCMH follows the trend of increasing interest in exploring this model of health care delivery [15,16]. It is also important to note that the intervention and IC groups received the same number of group visits and phone calls, the only difference being the content and the extra 1:1 PEA sessions received by the intervention group.

The few weaknesses include that the PEAs could not be blinded to treatment allocation, and generalizability is limited by the mostly non-Hispanic white population and that only 8.3% of the study population had an annual household income of less than $20,000. All parents included in this study were on the high end of the obese range (BMI 30–39.9), with baseline BMI values of 37.2 and 36.2 in the intervention and IC groups respectively. In addition, the age of the children included in the study were on the high end of the designated 2- to 5-year-old range: 4.6 years (IC) and 4.4 years (intervention). Although findings were promising within this specific population, further research in younger and more diverse populations is necessary [11].

Finally, it is unclear whether this intervention is scalable, and a cost-effectiveness analysis of this intervention is needed. This study was designed to limit the PCP’s role and simplify the process of identifying and intervening on overweight children and their parents, yet this required 3 part-time PEAs and a project coordinator responsible for delivering all of the group sessions and providing follow-up counseling to both intervention and IC groups.

Applications for Clinical Practice

This study demonstrates that in a mostly white, urban/suburban population, a parenting and behavior modification intervention focused on both parent and child leads to greater improvements in %0BMI and z-BMI in the child and BMI reduction in parents compared with an intervention focused on the child alone within pediatric PCMH practices. This intervention should be tested in more diverse populations. This study also suggests further exploration of the use of PEAs to help clinicians address obesity within the PCMH model of primary care.

—Natalie Berner, BA, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the efficacy, both short and long term, of a behavioral intervention targeting overweight parents and their children simultaneously versus an intervention focused on weight management only for the child within the patient-centered medical home (PCMH).

Design. 4-center, 2-arm, randomized controlled trial.

Setting and participants. Study participants were recruited from 4 urban/suburban pediatric practices. Primary care providers (PCPs) recruited patients at the time of well or sick visits based on body mass index (BMI) flagged prior to the visit by Patient Enhancement Assistants (PEAs). 171 parent/child dyads were assessed for eligibility and 105 were randomized in blocks of 12 dyads using a random number generator and stratified by child’s gender. Pediatricians were blind to their patient’s group assignments. Inclusion criteria were as follows: children aged 2–5 with a BMI higher than the 85th percentile for both age and gender, and 1 parent with a BMI greater than 25. Exclusion criteria were limited to children who were small for gestational age and/or short stature, and child or parent inability to perform physical activity. Specific precautions were taken to prevent contamination between intervention and information control (IC) groups [1].

Intervention. Three PEAs who held a masters or bachelors degree in psychology, nutrition, exercise science, or equivalent, or were registered dietitians, were embedded within each PCMH practice. For both the intervention and IC groups, parents attended 13 one-hour group sessions led by a PEA over a 12-month period, followed by a 12-month follow-up period with 3 additional meetings. The PEA telephoned parents between scheduled meetings. Pediatricians reviewed child’s weight changes every 6 months during scheduled appointments and the PEA sent progress notes in between these visits [2]. Dietary, physical, and sedentary activity guidelines were given based on the recommendations of a national multi-organizational expert committee [3]. Parents were given specific goals for their child, including a 0.5- to 1-pound per week loss, 60 minutes per day of physical activity, and limiting TV and screen time to less than 2 hours per day.

In addition, the intervention group received parenting and behavior change strategies to promote both parent and child weight loss. Parents were instructed to weigh themselves and their child once per week and monitor physical activity and diet. They received individual meetings with the PEA before or after group meetings to review goal setting and food/physical activity diaries. Parents were also given a weight loss goal of 1 to 2 pounds per week and were advised to model physical activity by engaging in active play with their child for at least 10 minutes per day.

Main outcome measures. The main outcome measures were %0BMI and z-BMI. Percent 0BMI is defined as [(child’s BMI – 50th percentile BMI)/50th percentile BMI] x 100 [2]. The authors chose %0BMI as the primary outcome measure because z-BMI can diminish the effect of weight change in heavier children [4]. Both measures were expressed as mean ± standard error (SEM). Parent weight change was measured using BMI alone.

The child’s weight was measured at each session and height was measured at baseline, 3, 6, 12, 18, and 24 months. Parent weight was measured every session in the intervention group, but only at baseline, 6, 12, 18, and 24 months in the IC group. A standardized protocol was followed for all height and weight measurements. An intention to treat analysis (ITT) was conducted on all parent/child dyads, regardless of whether or not they completed the study (n = 96).

Results. Research assistants assessed 171 parent/child dyads for eligibility. 66 were excluded for either not meeting inclusion criteria (n = 24) or declining to participate (n = 42). 105 dyads were randomized, but 9 did not receive the allocated intervention because they did not start the study, resulting in a total of 96 dyads included in analysis: 46 in the intervention group and 50 in the IC. Twelve- and 24-month completion rates were 83% and 73% respectively; there was no difference in attrition between intervention and IC groups.

The mean child ages of the intervention and IC groups were 4.6 ± 0.2 and 4.4 ± 0.2 years, respectively. 33 of the 46 children in the intervention group and 37 of the 50 children in the IC group were identified as non-Hispanic white. The mean yearly income of all families was $65,729 ± $3068, with only 8.3% of families below $20,000.

The intervention group had greater decreases in child %0BMI from baseline to 6, 12, 18, and 24 months than the IC group. Similar trends were seen with child z-BMI. A slower increase in height was observed in the intervention group when compared with the IC at both 18 months (P < 0.001) and at 24 months (P < 0.02). Parents showed greater overall BMI reduction in the intervention group as opposed to the IC group at all time points (P < 0.001). BMI changes achieved at 6 months were maintained at 24 months. %0BMI and parent BMI changes were correlated from baseline to 12, 18, and 24 months. No significant baseline moderators were found among the children in either group.

Conclusion. This study demonstrated that within the PCMH model of pediatric primary care, an intervention focused on joint behavior change and weight modification treatment of parents and children led to better initial and sustained improvements in %0BMI and z-BMI (in children) and BMI (in parents) than a child-focused IC.

Commentary

Over one-third of children and adolescents are considered to be overweight or have obesity, a number that has doubled in the past 30 years [5]. Pediatrician and primary care physician visits are optimal places to identify overweight children who are at risk for obesity and begin prevention measures, although identifying overweight and obese younger children can be difficult [6]. This study used PEAs to aid physicians in identification, implementation, and delivery. With increasing evidence to support pediatrician involvement in intensive weight management in a primary versus specialty care setting, embedding PEAs within the PCMH model may be an important way to help deliver care for overweight/obese children [7].

Although many approaches have been considered to target childhood obesity, this study represents an important contribution to the literature because it demonstrates that a primary care–based intervention targeting parents as well as their young children is more efficacious for weight management than a more traditional, child-only focused intervention. In addition, the intervention included many different evidence-based components such as teaching behavior modification techniques to parents, consideration of parenting styles and techniques, and encouraging simultaneous parental weight modification. While the U.S. Preventive Services Task Force (USPSTF) recommends intensive interventions with 30 sessions over 2 years [8], this study was able to accomplish significant weight change in 13 sessions.

This intervention is unique in its integration of parenting techniques with other evidence-based strategies for child weight management. Although it has been shown in the literature that certain parenting styles can positively impact children’s health behaviors [9], namely the use of positive reinforcement and monitoring children’s health practices [10], only a few studies have looked at the impact of parenting interventions on childhood obesity. Mazzeo et al demonstrated a significant reduction in child BMI with a parenting-only intervention in the NOURISH trial [11], Slusser et al found a significant child BMI reduction using parent training for low-income, 2- to 4-year-old children [12], and Brotman et al conducted a longitudinal study demonstrating that a family intervention could decrease BMI and improve overall child health behaviors [13]. Despite these aforementioned studies, there is a lack of longitudinal data on the association between general parenting style and weight [14], and this study addresses this gap in literature by providing 2-year follow-up and demonstrating sustained impact on the intervention group.

This study had many additional strengths, including randomized design, primary care physician blinding, use of intention to treat analysis, standardization of measurement tools, clear justification of sample size, long-term follow-up, and the use of child-appropriate BMI measures (eg, %0BMI vs. z-BMI as primary outcome measure). In addition, the intervention setting in a PCMH follows the trend of increasing interest in exploring this model of health care delivery [15,16]. It is also important to note that the intervention and IC groups received the same number of group visits and phone calls, the only difference being the content and the extra 1:1 PEA sessions received by the intervention group.

The few weaknesses include that the PEAs could not be blinded to treatment allocation, and generalizability is limited by the mostly non-Hispanic white population and that only 8.3% of the study population had an annual household income of less than $20,000. All parents included in this study were on the high end of the obese range (BMI 30–39.9), with baseline BMI values of 37.2 and 36.2 in the intervention and IC groups respectively. In addition, the age of the children included in the study were on the high end of the designated 2- to 5-year-old range: 4.6 years (IC) and 4.4 years (intervention). Although findings were promising within this specific population, further research in younger and more diverse populations is necessary [11].

Finally, it is unclear whether this intervention is scalable, and a cost-effectiveness analysis of this intervention is needed. This study was designed to limit the PCP’s role and simplify the process of identifying and intervening on overweight children and their parents, yet this required 3 part-time PEAs and a project coordinator responsible for delivering all of the group sessions and providing follow-up counseling to both intervention and IC groups.

Applications for Clinical Practice

This study demonstrates that in a mostly white, urban/suburban population, a parenting and behavior modification intervention focused on both parent and child leads to greater improvements in %0BMI and z-BMI in the child and BMI reduction in parents compared with an intervention focused on the child alone within pediatric PCMH practices. This intervention should be tested in more diverse populations. This study also suggests further exploration of the use of PEAs to help clinicians address obesity within the PCMH model of primary care.

—Natalie Berner, BA, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the efficacy, both short and long term, of a behavioral intervention targeting overweight parents and their children simultaneously versus an intervention focused on weight management only for the child within the patient-centered medical home (PCMH).

Design. 4-center, 2-arm, randomized controlled trial.

Setting and participants. Study participants were recruited from 4 urban/suburban pediatric practices. Primary care providers (PCPs) recruited patients at the time of well or sick visits based on body mass index (BMI) flagged prior to the visit by Patient Enhancement Assistants (PEAs). 171 parent/child dyads were assessed for eligibility and 105 were randomized in blocks of 12 dyads using a random number generator and stratified by child’s gender. Pediatricians were blind to their patient’s group assignments. Inclusion criteria were as follows: children aged 2–5 with a BMI higher than the 85th percentile for both age and gender, and 1 parent with a BMI greater than 25. Exclusion criteria were limited to children who were small for gestational age and/or short stature, and child or parent inability to perform physical activity. Specific precautions were taken to prevent contamination between intervention and information control (IC) groups [1].

Intervention. Three PEAs who held a masters or bachelors degree in psychology, nutrition, exercise science, or equivalent, or were registered dietitians, were embedded within each PCMH practice. For both the intervention and IC groups, parents attended 13 one-hour group sessions led by a PEA over a 12-month period, followed by a 12-month follow-up period with 3 additional meetings. The PEA telephoned parents between scheduled meetings. Pediatricians reviewed child’s weight changes every 6 months during scheduled appointments and the PEA sent progress notes in between these visits [2]. Dietary, physical, and sedentary activity guidelines were given based on the recommendations of a national multi-organizational expert committee [3]. Parents were given specific goals for their child, including a 0.5- to 1-pound per week loss, 60 minutes per day of physical activity, and limiting TV and screen time to less than 2 hours per day.

In addition, the intervention group received parenting and behavior change strategies to promote both parent and child weight loss. Parents were instructed to weigh themselves and their child once per week and monitor physical activity and diet. They received individual meetings with the PEA before or after group meetings to review goal setting and food/physical activity diaries. Parents were also given a weight loss goal of 1 to 2 pounds per week and were advised to model physical activity by engaging in active play with their child for at least 10 minutes per day.

Main outcome measures. The main outcome measures were %0BMI and z-BMI. Percent 0BMI is defined as [(child’s BMI – 50th percentile BMI)/50th percentile BMI] x 100 [2]. The authors chose %0BMI as the primary outcome measure because z-BMI can diminish the effect of weight change in heavier children [4]. Both measures were expressed as mean ± standard error (SEM). Parent weight change was measured using BMI alone.

The child’s weight was measured at each session and height was measured at baseline, 3, 6, 12, 18, and 24 months. Parent weight was measured every session in the intervention group, but only at baseline, 6, 12, 18, and 24 months in the IC group. A standardized protocol was followed for all height and weight measurements. An intention to treat analysis (ITT) was conducted on all parent/child dyads, regardless of whether or not they completed the study (n = 96).

Results. Research assistants assessed 171 parent/child dyads for eligibility. 66 were excluded for either not meeting inclusion criteria (n = 24) or declining to participate (n = 42). 105 dyads were randomized, but 9 did not receive the allocated intervention because they did not start the study, resulting in a total of 96 dyads included in analysis: 46 in the intervention group and 50 in the IC. Twelve- and 24-month completion rates were 83% and 73% respectively; there was no difference in attrition between intervention and IC groups.

The mean child ages of the intervention and IC groups were 4.6 ± 0.2 and 4.4 ± 0.2 years, respectively. 33 of the 46 children in the intervention group and 37 of the 50 children in the IC group were identified as non-Hispanic white. The mean yearly income of all families was $65,729 ± $3068, with only 8.3% of families below $20,000.

The intervention group had greater decreases in child %0BMI from baseline to 6, 12, 18, and 24 months than the IC group. Similar trends were seen with child z-BMI. A slower increase in height was observed in the intervention group when compared with the IC at both 18 months (P < 0.001) and at 24 months (P < 0.02). Parents showed greater overall BMI reduction in the intervention group as opposed to the IC group at all time points (P < 0.001). BMI changes achieved at 6 months were maintained at 24 months. %0BMI and parent BMI changes were correlated from baseline to 12, 18, and 24 months. No significant baseline moderators were found among the children in either group.

Conclusion. This study demonstrated that within the PCMH model of pediatric primary care, an intervention focused on joint behavior change and weight modification treatment of parents and children led to better initial and sustained improvements in %0BMI and z-BMI (in children) and BMI (in parents) than a child-focused IC.

Commentary

Over one-third of children and adolescents are considered to be overweight or have obesity, a number that has doubled in the past 30 years [5]. Pediatrician and primary care physician visits are optimal places to identify overweight children who are at risk for obesity and begin prevention measures, although identifying overweight and obese younger children can be difficult [6]. This study used PEAs to aid physicians in identification, implementation, and delivery. With increasing evidence to support pediatrician involvement in intensive weight management in a primary versus specialty care setting, embedding PEAs within the PCMH model may be an important way to help deliver care for overweight/obese children [7].

Although many approaches have been considered to target childhood obesity, this study represents an important contribution to the literature because it demonstrates that a primary care–based intervention targeting parents as well as their young children is more efficacious for weight management than a more traditional, child-only focused intervention. In addition, the intervention included many different evidence-based components such as teaching behavior modification techniques to parents, consideration of parenting styles and techniques, and encouraging simultaneous parental weight modification. While the U.S. Preventive Services Task Force (USPSTF) recommends intensive interventions with 30 sessions over 2 years [8], this study was able to accomplish significant weight change in 13 sessions.

This intervention is unique in its integration of parenting techniques with other evidence-based strategies for child weight management. Although it has been shown in the literature that certain parenting styles can positively impact children’s health behaviors [9], namely the use of positive reinforcement and monitoring children’s health practices [10], only a few studies have looked at the impact of parenting interventions on childhood obesity. Mazzeo et al demonstrated a significant reduction in child BMI with a parenting-only intervention in the NOURISH trial [11], Slusser et al found a significant child BMI reduction using parent training for low-income, 2- to 4-year-old children [12], and Brotman et al conducted a longitudinal study demonstrating that a family intervention could decrease BMI and improve overall child health behaviors [13]. Despite these aforementioned studies, there is a lack of longitudinal data on the association between general parenting style and weight [14], and this study addresses this gap in literature by providing 2-year follow-up and demonstrating sustained impact on the intervention group.

This study had many additional strengths, including randomized design, primary care physician blinding, use of intention to treat analysis, standardization of measurement tools, clear justification of sample size, long-term follow-up, and the use of child-appropriate BMI measures (eg, %0BMI vs. z-BMI as primary outcome measure). In addition, the intervention setting in a PCMH follows the trend of increasing interest in exploring this model of health care delivery [15,16]. It is also important to note that the intervention and IC groups received the same number of group visits and phone calls, the only difference being the content and the extra 1:1 PEA sessions received by the intervention group.

The few weaknesses include that the PEAs could not be blinded to treatment allocation, and generalizability is limited by the mostly non-Hispanic white population and that only 8.3% of the study population had an annual household income of less than $20,000. All parents included in this study were on the high end of the obese range (BMI 30–39.9), with baseline BMI values of 37.2 and 36.2 in the intervention and IC groups respectively. In addition, the age of the children included in the study were on the high end of the designated 2- to 5-year-old range: 4.6 years (IC) and 4.4 years (intervention). Although findings were promising within this specific population, further research in younger and more diverse populations is necessary [11].

Finally, it is unclear whether this intervention is scalable, and a cost-effectiveness analysis of this intervention is needed. This study was designed to limit the PCP’s role and simplify the process of identifying and intervening on overweight children and their parents, yet this required 3 part-time PEAs and a project coordinator responsible for delivering all of the group sessions and providing follow-up counseling to both intervention and IC groups.

Applications for Clinical Practice

This study demonstrates that in a mostly white, urban/suburban population, a parenting and behavior modification intervention focused on both parent and child leads to greater improvements in %0BMI and z-BMI in the child and BMI reduction in parents compared with an intervention focused on the child alone within pediatric PCMH practices. This intervention should be tested in more diverse populations. This study also suggests further exploration of the use of PEAs to help clinicians address obesity within the PCMH model of primary care.

—Natalie Berner, BA, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the effect of phentermine and topiramate extended release (PHEN/TPM ER) treatment on progression to type 2 diabetes and/or cardiometabolic disease in subjects with prediabetes and/or metabolic syndrome (MetS) at baseline.

Design. Sub-group analysis of a larger double-blind, randomized, placebo-controlled trial of PHEN/TPM ER in overweight and obese adults.

Setting and participants. The larger study had 2 phases —a 56-week weight loss trial (CONQUER, n = 866), followed by an extension of the drug trial out to 108 weeks (SEQUEL, n = 675) in a sub-group of CONQUER participants. The CONQUER trial, based at 93 U.S. centers, enrolled overweight or obese patients with at least 2 obesity-related comorbidities and randomly assigned them to receive either placebo or PHEN/TPM ER at a lower (7.5 mg/46 mg) or higher (15 mg/92 mg) daily dose. All 3 groups also received lifestyle modification counseling that included an evidence-based diet and exercise curriculum. Participants received study drug and lifestyle counseling in the setting of monthly visits during the 60- (CONQUER) or 108-week (SEQUEL) follow-up period.

The analyses presented in this paper focus on the 475 participants who completed both CONQUER and SEQUEL and who were characterized as pre-diabetic or as having the metabolic syndrome (MetS) at baseline. Pre-diabetes was defined as having a blood glucose level of 100–125 mg/dL or higher while fasting, or 140–199 mg/dL after an oral glucose tolerance test (GTT). MetS was characterized in participants who displayed 3 or more of the following at baseline: waist circumference ≥ 102 cm in men or 88 cm in women; triglycerides ≥ 150 mg/dL or on a lipid-lowering medication; HDL < 40 mg/dL in men or < 50 mg/dL in women; systolic BP ≥ 130 mm Hg or diastolic BP ≥ 85 mm Hg (or on antihypertensive); and fasting glucose ≥ 100 mg/dL or on treatment for elevated glucose.

Main outcome measures. The primary outcome for this study was percent weight loss at 108 weeks of follow-up (or early termination). Secondary outcomes included cardiometabolic changes, such as development of type 2 diabetes and changes in lipid measures, blood pressure, and waist circumference. These were assessed at baseline, week 56, and week 108 (or at early termination). Rates of progression to type 2 diabetes were compared between the treatment groups using chi-square testing. Intention-to-treat (ITT) ANCOVA analysis was performed with multiple imputation techniques to address missing data, as well as with an alternative analysis using last observation carried forward.

Results. The study arms were similar with respect to baseline characteristics. Average age was 51 years in the high dose PHEN/TPM ER arm and 52 in the other arms. Over half (65%) of participants were women and 86% were Caucasian. Mean BMI was 36 kg/m² (class II obesity). Over half of participants were on antihypertensive medications at baseline but with well-controlled blood pressure (mean 128/80 mm Hg). Of the 475 people in this analysis, 316 met criteria for prediabetes, 451 for MetS, and 292 for both prediabetes and MetS.

Weight loss at 2 years was significantly greater in subjects taking PHEN/TPM ER (10.9% in the lower dose group, 12.1% in the higher dose group) compared to those taking placebo (2.5%) (P < 0.001). Mirroring weight loss results, type 2 diabetes incidence was also significantly lower in the drug treatment arms than in the placebo arm at 2 years after randomization—annualized incidence was 6.1% for placebo vs. 1.8% for lower-dose drug and 1.3% for higher-dose drug (P < 0.05). Greater weight loss was associated with greater decrease in diabetes incidence across all 3 arms of the study. Those persons who did not achieve at least a 5% weight loss at 2 years had the highest annualized risk of developing diabetes (6.3%), compared with a 0.9% risk among those who lost at least 15% of their weight. Improvements in other cardiometabolic parameters, including HDL, triglycerides, waist circumference, and insulin sensitivity index, was more common among the PHEN/TPM ER participants compared with placebo. Blood pressure decreased slightly for all 3 groups and there was no significant difference between the drug arms and the placebo arm.

Discontinuation of study medication occurred in all 3 groups (3.1% in placebo, 6.1% in lower-dose medication, and 5.5% in higher-dose medication), with serious adverse events in 5%, 7%, and 8.5%, respectively. There were no deaths.

Conclusion. PHEN/TPM ER administered over a 2-year period significantly improved weight loss and decreased progression to type 2 diabetes relative to placebo in a group of at-risk participants.

Commentary

Diabetes and related cardiometabolic disease are major contributors to morbidity and mortality in adults. With the exception of invasive treatments such as bariatric surgery, reversal of diabetes once it is established has proven quite difficult [1,2], and thus there is an increased emphasis from the public health and medical communities on preventing the development of this disease in the first place. Complicating the picture, recently broadened criteria for pre-diabetes will likely result in a very large number of these at-risk individuals being identified [3,4]. Although intensive lifestyle interventions resulting in a 5% to 7% weight loss among pre-diabetics have been shown to delay progression to diabetes [5], the translation of these programs into real-world settings has, so far, shown less promise than the original randomized trials might have indicated [4]. Although there is ongoing work to try to improve results and uptake in community-based lifestyle intervention programs, for many patients and clinicians these resource-intensive programs currently prove difficult to do well on a large scale.

Alternative methods of helping patients achieve and maintain that critical > 5% weight loss are desperately needed, not only for preventing diabetes, but also for impacting the numerous other risks associated with obesity. This particular trial capitalized on the notion that it is probably successful weight loss, not the intervention format used to achieve that weight loss, which drives decreased diabetes risk. This study was a sub-analysis of a larger randomized trial, and many of the strengths of that larger study are therefore reflected in this paper. Participants and study staff were blinded to treatment arm with the use of placebo, a very important strength when adverse reactions and drug intolerances need to be measured. Furthermore, this likely equalized motivation to comply with the lifestyle recommendations across the treatment arms—this might not have been the case if patients were aware that they were or were not receiving study drug. Another key strength of the study is its duration. PHEN/TPM ER is unique in that it is approved by the FDA for long-term use. Whereas many studies of weight loss show maximum intervention effect at about 6 months followed by weight regain, this study showed sustained weight loss up to 2 years after starting therapy, presumably because participants could actually continue the therapy for the full 2 years. Most importantly, the intervention itself (medication plus low-intensity lifestyle counseling) is likely highly replicable in clinical practice.

There are some important limitations to consider when interpreting the results from this study. First, the participants analyzed in this paper were comprised entirely of people who had already participated in a full year of the parent study and therefore probably represent a sub-group that might have been experiencing greater success as a result of their participation, potentially generating an overly optimistic estimate of weight loss and health effect for all of the groups relative to what might be seen in a general population. This feature of the design also limits this study’s ability to comment on drug intolerance or early adverse reactions—those who didn’t stick with the pills for at least a year would not have been included in these analyses. In terms of generalizability, although the infrastructure required from a clinical standpoint is much lower for an intervention like this (prescribing a medication) compared to an intensive lifestyle intervention, these drugs are still costly, and many insurers/providers may not offer them on formulary. Thus, to realize the long-term benefits of sustained weight loss, patients may need to face significant out-of-pocket costs, which may limit uptake of this therapy to those with financial means. For this and other reasons, it will be important to do future studies looking at how quickly weight is re-gained once people stop taking the medication. Another threat to generalizability is the racial makeup of the participants—the vast majority of them were non-Hispanic white. Furthermore, although a majority of the participants had hypertension, it was well-controlled in all (a prerequisite for taking the medication), and it is unclear whether in a real-world patient population hypertension would be adequately controlled in a large number of patients.

Another issue to consider when looking at the use of weight loss medications for prevention of diabetes is the relative risk of prolonged medication use compared with the risk for developing diabetes. Clearly, for obese patients who are interested in losing weight for other reasons, prevention of diabetes is a wonderful side effect of achieving that goal. However, it is worth noting that even in the highest-risk group of participants in this study (those who lost < 5% of weight), the annualized risk of developing diabetes was about 6% (< 20% cumulative risk projected over 3 years). Compare this to the 7% to 8% serious adverse event rate observed in those on drug therapy. Although the medication did reduce annualized diabetes risk significantly, the vast majority of people in all the arms did not develop diabetes during follow-up. This drives home the point that our current categorization of pre-diabetes is far from perfect in identifying people who are at imminent risk of becoming diabetic, and reinforces the notion that any treatment we provide to them in the name of diabetes prevention should be free from risk of harm. Rather than applying a long-term medication with potentially harmful side effects to a large group of at-risk patients, more research is needed to provide tools for clinicians to think carefully about which of their patients are truly at highest risk of going on to develop diabetes in the near future.

Applications for Clinical Practice

Although clinicians ought not use PHEN/TPM ER exclusively for diabetes prevention based on the results from this trial, delay of diabetes onset is a possible and important benefit of the use of PHEN/TPM ER in obese patients, provided that they are willing to also make and sustain lifestyle changes in order to lose a clinically significant amount of weight.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine the effect of phentermine and topiramate extended release (PHEN/TPM ER) treatment on progression to type 2 diabetes and/or cardiometabolic disease in subjects with prediabetes and/or metabolic syndrome (MetS) at baseline.

Design. Sub-group analysis of a larger double-blind, randomized, placebo-controlled trial of PHEN/TPM ER in overweight and obese adults.

Setting and participants. The larger study had 2 phases —a 56-week weight loss trial (CONQUER, n = 866), followed by an extension of the drug trial out to 108 weeks (SEQUEL, n = 675) in a sub-group of CONQUER participants. The CONQUER trial, based at 93 U.S. centers, enrolled overweight or obese patients with at least 2 obesity-related comorbidities and randomly assigned them to receive either placebo or PHEN/TPM ER at a lower (7.5 mg/46 mg) or higher (15 mg/92 mg) daily dose. All 3 groups also received lifestyle modification counseling that included an evidence-based diet and exercise curriculum. Participants received study drug and lifestyle counseling in the setting of monthly visits during the 60- (CONQUER) or 108-week (SEQUEL) follow-up period.

The analyses presented in this paper focus on the 475 participants who completed both CONQUER and SEQUEL and who were characterized as pre-diabetic or as having the metabolic syndrome (MetS) at baseline. Pre-diabetes was defined as having a blood glucose level of 100–125 mg/dL or higher while fasting, or 140–199 mg/dL after an oral glucose tolerance test (GTT). MetS was characterized in participants who displayed 3 or more of the following at baseline: waist circumference ≥ 102 cm in men or 88 cm in women; triglycerides ≥ 150 mg/dL or on a lipid-lowering medication; HDL < 40 mg/dL in men or < 50 mg/dL in women; systolic BP ≥ 130 mm Hg or diastolic BP ≥ 85 mm Hg (or on antihypertensive); and fasting glucose ≥ 100 mg/dL or on treatment for elevated glucose.

Main outcome measures. The primary outcome for this study was percent weight loss at 108 weeks of follow-up (or early termination). Secondary outcomes included cardiometabolic changes, such as development of type 2 diabetes and changes in lipid measures, blood pressure, and waist circumference. These were assessed at baseline, week 56, and week 108 (or at early termination). Rates of progression to type 2 diabetes were compared between the treatment groups using chi-square testing. Intention-to-treat (ITT) ANCOVA analysis was performed with multiple imputation techniques to address missing data, as well as with an alternative analysis using last observation carried forward.

Results. The study arms were similar with respect to baseline characteristics. Average age was 51 years in the high dose PHEN/TPM ER arm and 52 in the other arms. Over half (65%) of participants were women and 86% were Caucasian. Mean BMI was 36 kg/m² (class II obesity). Over half of participants were on antihypertensive medications at baseline but with well-controlled blood pressure (mean 128/80 mm Hg). Of the 475 people in this analysis, 316 met criteria for prediabetes, 451 for MetS, and 292 for both prediabetes and MetS.

Weight loss at 2 years was significantly greater in subjects taking PHEN/TPM ER (10.9% in the lower dose group, 12.1% in the higher dose group) compared to those taking placebo (2.5%) (P < 0.001). Mirroring weight loss results, type 2 diabetes incidence was also significantly lower in the drug treatment arms than in the placebo arm at 2 years after randomization—annualized incidence was 6.1% for placebo vs. 1.8% for lower-dose drug and 1.3% for higher-dose drug (P < 0.05). Greater weight loss was associated with greater decrease in diabetes incidence across all 3 arms of the study. Those persons who did not achieve at least a 5% weight loss at 2 years had the highest annualized risk of developing diabetes (6.3%), compared with a 0.9% risk among those who lost at least 15% of their weight. Improvements in other cardiometabolic parameters, including HDL, triglycerides, waist circumference, and insulin sensitivity index, was more common among the PHEN/TPM ER participants compared with placebo. Blood pressure decreased slightly for all 3 groups and there was no significant difference between the drug arms and the placebo arm.

Discontinuation of study medication occurred in all 3 groups (3.1% in placebo, 6.1% in lower-dose medication, and 5.5% in higher-dose medication), with serious adverse events in 5%, 7%, and 8.5%, respectively. There were no deaths.

Conclusion. PHEN/TPM ER administered over a 2-year period significantly improved weight loss and decreased progression to type 2 diabetes relative to placebo in a group of at-risk participants.

Commentary

Diabetes and related cardiometabolic disease are major contributors to morbidity and mortality in adults. With the exception of invasive treatments such as bariatric surgery, reversal of diabetes once it is established has proven quite difficult [1,2], and thus there is an increased emphasis from the public health and medical communities on preventing the development of this disease in the first place. Complicating the picture, recently broadened criteria for pre-diabetes will likely result in a very large number of these at-risk individuals being identified [3,4]. Although intensive lifestyle interventions resulting in a 5% to 7% weight loss among pre-diabetics have been shown to delay progression to diabetes [5], the translation of these programs into real-world settings has, so far, shown less promise than the original randomized trials might have indicated [4]. Although there is ongoing work to try to improve results and uptake in community-based lifestyle intervention programs, for many patients and clinicians these resource-intensive programs currently prove difficult to do well on a large scale.

Alternative methods of helping patients achieve and maintain that critical > 5% weight loss are desperately needed, not only for preventing diabetes, but also for impacting the numerous other risks associated with obesity. This particular trial capitalized on the notion that it is probably successful weight loss, not the intervention format used to achieve that weight loss, which drives decreased diabetes risk. This study was a sub-analysis of a larger randomized trial, and many of the strengths of that larger study are therefore reflected in this paper. Participants and study staff were blinded to treatment arm with the use of placebo, a very important strength when adverse reactions and drug intolerances need to be measured. Furthermore, this likely equalized motivation to comply with the lifestyle recommendations across the treatment arms—this might not have been the case if patients were aware that they were or were not receiving study drug. Another key strength of the study is its duration. PHEN/TPM ER is unique in that it is approved by the FDA for long-term use. Whereas many studies of weight loss show maximum intervention effect at about 6 months followed by weight regain, this study showed sustained weight loss up to 2 years after starting therapy, presumably because participants could actually continue the therapy for the full 2 years. Most importantly, the intervention itself (medication plus low-intensity lifestyle counseling) is likely highly replicable in clinical practice.

There are some important limitations to consider when interpreting the results from this study. First, the participants analyzed in this paper were comprised entirely of people who had already participated in a full year of the parent study and therefore probably represent a sub-group that might have been experiencing greater success as a result of their participation, potentially generating an overly optimistic estimate of weight loss and health effect for all of the groups relative to what might be seen in a general population. This feature of the design also limits this study’s ability to comment on drug intolerance or early adverse reactions—those who didn’t stick with the pills for at least a year would not have been included in these analyses. In terms of generalizability, although the infrastructure required from a clinical standpoint is much lower for an intervention like this (prescribing a medication) compared to an intensive lifestyle intervention, these drugs are still costly, and many insurers/providers may not offer them on formulary. Thus, to realize the long-term benefits of sustained weight loss, patients may need to face significant out-of-pocket costs, which may limit uptake of this therapy to those with financial means. For this and other reasons, it will be important to do future studies looking at how quickly weight is re-gained once people stop taking the medication. Another threat to generalizability is the racial makeup of the participants—the vast majority of them were non-Hispanic white. Furthermore, although a majority of the participants had hypertension, it was well-controlled in all (a prerequisite for taking the medication), and it is unclear whether in a real-world patient population hypertension would be adequately controlled in a large number of patients.

Another issue to consider when looking at the use of weight loss medications for prevention of diabetes is the relative risk of prolonged medication use compared with the risk for developing diabetes. Clearly, for obese patients who are interested in losing weight for other reasons, prevention of diabetes is a wonderful side effect of achieving that goal. However, it is worth noting that even in the highest-risk group of participants in this study (those who lost < 5% of weight), the annualized risk of developing diabetes was about 6% (< 20% cumulative risk projected over 3 years). Compare this to the 7% to 8% serious adverse event rate observed in those on drug therapy. Although the medication did reduce annualized diabetes risk significantly, the vast majority of people in all the arms did not develop diabetes during follow-up. This drives home the point that our current categorization of pre-diabetes is far from perfect in identifying people who are at imminent risk of becoming diabetic, and reinforces the notion that any treatment we provide to them in the name of diabetes prevention should be free from risk of harm. Rather than applying a long-term medication with potentially harmful side effects to a large group of at-risk patients, more research is needed to provide tools for clinicians to think carefully about which of their patients are truly at highest risk of going on to develop diabetes in the near future.

Applications for Clinical Practice

Although clinicians ought not use PHEN/TPM ER exclusively for diabetes prevention based on the results from this trial, delay of diabetes onset is a possible and important benefit of the use of PHEN/TPM ER in obese patients, provided that they are willing to also make and sustain lifestyle changes in order to lose a clinically significant amount of weight.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine the effect of phentermine and topiramate extended release (PHEN/TPM ER) treatment on progression to type 2 diabetes and/or cardiometabolic disease in subjects with prediabetes and/or metabolic syndrome (MetS) at baseline.

Design. Sub-group analysis of a larger double-blind, randomized, placebo-controlled trial of PHEN/TPM ER in overweight and obese adults.

Setting and participants. The larger study had 2 phases —a 56-week weight loss trial (CONQUER, n = 866), followed by an extension of the drug trial out to 108 weeks (SEQUEL, n = 675) in a sub-group of CONQUER participants. The CONQUER trial, based at 93 U.S. centers, enrolled overweight or obese patients with at least 2 obesity-related comorbidities and randomly assigned them to receive either placebo or PHEN/TPM ER at a lower (7.5 mg/46 mg) or higher (15 mg/92 mg) daily dose. All 3 groups also received lifestyle modification counseling that included an evidence-based diet and exercise curriculum. Participants received study drug and lifestyle counseling in the setting of monthly visits during the 60- (CONQUER) or 108-week (SEQUEL) follow-up period.

The analyses presented in this paper focus on the 475 participants who completed both CONQUER and SEQUEL and who were characterized as pre-diabetic or as having the metabolic syndrome (MetS) at baseline. Pre-diabetes was defined as having a blood glucose level of 100–125 mg/dL or higher while fasting, or 140–199 mg/dL after an oral glucose tolerance test (GTT). MetS was characterized in participants who displayed 3 or more of the following at baseline: waist circumference ≥ 102 cm in men or 88 cm in women; triglycerides ≥ 150 mg/dL or on a lipid-lowering medication; HDL < 40 mg/dL in men or < 50 mg/dL in women; systolic BP ≥ 130 mm Hg or diastolic BP ≥ 85 mm Hg (or on antihypertensive); and fasting glucose ≥ 100 mg/dL or on treatment for elevated glucose.

Main outcome measures. The primary outcome for this study was percent weight loss at 108 weeks of follow-up (or early termination). Secondary outcomes included cardiometabolic changes, such as development of type 2 diabetes and changes in lipid measures, blood pressure, and waist circumference. These were assessed at baseline, week 56, and week 108 (or at early termination). Rates of progression to type 2 diabetes were compared between the treatment groups using chi-square testing. Intention-to-treat (ITT) ANCOVA analysis was performed with multiple imputation techniques to address missing data, as well as with an alternative analysis using last observation carried forward.

Results. The study arms were similar with respect to baseline characteristics. Average age was 51 years in the high dose PHEN/TPM ER arm and 52 in the other arms. Over half (65%) of participants were women and 86% were Caucasian. Mean BMI was 36 kg/m² (class II obesity). Over half of participants were on antihypertensive medications at baseline but with well-controlled blood pressure (mean 128/80 mm Hg). Of the 475 people in this analysis, 316 met criteria for prediabetes, 451 for MetS, and 292 for both prediabetes and MetS.

Weight loss at 2 years was significantly greater in subjects taking PHEN/TPM ER (10.9% in the lower dose group, 12.1% in the higher dose group) compared to those taking placebo (2.5%) (P < 0.001). Mirroring weight loss results, type 2 diabetes incidence was also significantly lower in the drug treatment arms than in the placebo arm at 2 years after randomization—annualized incidence was 6.1% for placebo vs. 1.8% for lower-dose drug and 1.3% for higher-dose drug (P < 0.05). Greater weight loss was associated with greater decrease in diabetes incidence across all 3 arms of the study. Those persons who did not achieve at least a 5% weight loss at 2 years had the highest annualized risk of developing diabetes (6.3%), compared with a 0.9% risk among those who lost at least 15% of their weight. Improvements in other cardiometabolic parameters, including HDL, triglycerides, waist circumference, and insulin sensitivity index, was more common among the PHEN/TPM ER participants compared with placebo. Blood pressure decreased slightly for all 3 groups and there was no significant difference between the drug arms and the placebo arm.

Discontinuation of study medication occurred in all 3 groups (3.1% in placebo, 6.1% in lower-dose medication, and 5.5% in higher-dose medication), with serious adverse events in 5%, 7%, and 8.5%, respectively. There were no deaths.

Conclusion. PHEN/TPM ER administered over a 2-year period significantly improved weight loss and decreased progression to type 2 diabetes relative to placebo in a group of at-risk participants.

Commentary

Diabetes and related cardiometabolic disease are major contributors to morbidity and mortality in adults. With the exception of invasive treatments such as bariatric surgery, reversal of diabetes once it is established has proven quite difficult [1,2], and thus there is an increased emphasis from the public health and medical communities on preventing the development of this disease in the first place. Complicating the picture, recently broadened criteria for pre-diabetes will likely result in a very large number of these at-risk individuals being identified [3,4]. Although intensive lifestyle interventions resulting in a 5% to 7% weight loss among pre-diabetics have been shown to delay progression to diabetes [5], the translation of these programs into real-world settings has, so far, shown less promise than the original randomized trials might have indicated [4]. Although there is ongoing work to try to improve results and uptake in community-based lifestyle intervention programs, for many patients and clinicians these resource-intensive programs currently prove difficult to do well on a large scale.

Alternative methods of helping patients achieve and maintain that critical > 5% weight loss are desperately needed, not only for preventing diabetes, but also for impacting the numerous other risks associated with obesity. This particular trial capitalized on the notion that it is probably successful weight loss, not the intervention format used to achieve that weight loss, which drives decreased diabetes risk. This study was a sub-analysis of a larger randomized trial, and many of the strengths of that larger study are therefore reflected in this paper. Participants and study staff were blinded to treatment arm with the use of placebo, a very important strength when adverse reactions and drug intolerances need to be measured. Furthermore, this likely equalized motivation to comply with the lifestyle recommendations across the treatment arms—this might not have been the case if patients were aware that they were or were not receiving study drug. Another key strength of the study is its duration. PHEN/TPM ER is unique in that it is approved by the FDA for long-term use. Whereas many studies of weight loss show maximum intervention effect at about 6 months followed by weight regain, this study showed sustained weight loss up to 2 years after starting therapy, presumably because participants could actually continue the therapy for the full 2 years. Most importantly, the intervention itself (medication plus low-intensity lifestyle counseling) is likely highly replicable in clinical practice.

There are some important limitations to consider when interpreting the results from this study. First, the participants analyzed in this paper were comprised entirely of people who had already participated in a full year of the parent study and therefore probably represent a sub-group that might have been experiencing greater success as a result of their participation, potentially generating an overly optimistic estimate of weight loss and health effect for all of the groups relative to what might be seen in a general population. This feature of the design also limits this study’s ability to comment on drug intolerance or early adverse reactions—those who didn’t stick with the pills for at least a year would not have been included in these analyses. In terms of generalizability, although the infrastructure required from a clinical standpoint is much lower for an intervention like this (prescribing a medication) compared to an intensive lifestyle intervention, these drugs are still costly, and many insurers/providers may not offer them on formulary. Thus, to realize the long-term benefits of sustained weight loss, patients may need to face significant out-of-pocket costs, which may limit uptake of this therapy to those with financial means. For this and other reasons, it will be important to do future studies looking at how quickly weight is re-gained once people stop taking the medication. Another threat to generalizability is the racial makeup of the participants—the vast majority of them were non-Hispanic white. Furthermore, although a majority of the participants had hypertension, it was well-controlled in all (a prerequisite for taking the medication), and it is unclear whether in a real-world patient population hypertension would be adequately controlled in a large number of patients.

Another issue to consider when looking at the use of weight loss medications for prevention of diabetes is the relative risk of prolonged medication use compared with the risk for developing diabetes. Clearly, for obese patients who are interested in losing weight for other reasons, prevention of diabetes is a wonderful side effect of achieving that goal. However, it is worth noting that even in the highest-risk group of participants in this study (those who lost < 5% of weight), the annualized risk of developing diabetes was about 6% (< 20% cumulative risk projected over 3 years). Compare this to the 7% to 8% serious adverse event rate observed in those on drug therapy. Although the medication did reduce annualized diabetes risk significantly, the vast majority of people in all the arms did not develop diabetes during follow-up. This drives home the point that our current categorization of pre-diabetes is far from perfect in identifying people who are at imminent risk of becoming diabetic, and reinforces the notion that any treatment we provide to them in the name of diabetes prevention should be free from risk of harm. Rather than applying a long-term medication with potentially harmful side effects to a large group of at-risk patients, more research is needed to provide tools for clinicians to think carefully about which of their patients are truly at highest risk of going on to develop diabetes in the near future.

Applications for Clinical Practice

Although clinicians ought not use PHEN/TPM ER exclusively for diabetes prevention based on the results from this trial, delay of diabetes onset is a possible and important benefit of the use of PHEN/TPM ER in obese patients, provided that they are willing to also make and sustain lifestyle changes in order to lose a clinically significant amount of weight.

—Kristina Lewis, MD, MPH

Study Overview

Objective. To determine if a patient-oriented decision aid for prioritizing treatment goals in diabetes leads to changes in patient empowerment for setting and achieving goals and in treatment.

Design. Randomized controlled trial.

Setting and participants. Study participants were recruited from 18 general practices in the north of the Netherlands between April 2011 and August 2012. Participants were included if they had a diagnosis of type 2 diabetes and were managed in primary care. Participants were identified from the electronic medical record system and at least 40 patients were selected from each practice to be contacted for participation. Subjects were excluded if they had myocardial infarction in the preceding year, experienced a stroke, had heart failure, angina, or a terminal illness, or were more than 65 years of age when they received their diabetes diagnosis. Other exclusion criteria include dementia, cognitive deficits, blindness, or an inability to read Dutch. Eligibility criteria were confirmed with the health care provider from each practice. Practices that were included in the study had several features: (1) each had an electronic medical record system supporting structured care protocols; (2) most practices have a nurse practitioner or specialized assistant for diabetes care who carries out the quarterly diabetes checks and is trained to conduct physical examinations, risk assessments, patient education, and counseling; (3) all practices received training in motivational interviewing.

The decision aid format was either a computer screen or printed version, and presented as either a short version, showing treatment effects on myocardial infarction risk only, or as an extended version, including effects on additional outcomes (stroke, amputation, blindness, renal failure). Practices were randomly assigned to use the computer screen or printed version, stratified by practice size (< 2500 patients or > 2500 patients) and number of GPs (solo or several). Within each practice, consenting patients were randomized to receive the short version aid, the extended version, or to the control group.

Intervention. The decision aid presents individually tailored information on risks and treatment options for multiple risk factors. The aid focuses on shared goal setting and decision making, particularly with respect to the drug treatment of risk factors including hemoglobin A1c, systolic blood pressure, low density lipoprotein cholesterol, and smoking. The decision aid is designed to be used by patients before a regular check-up and discussed with their health care provider during a visit to help prioritize treatment that will maximize outcomes; the aid helps to summarize effects of the various treatment options. The patients were asked to come to the practice 15 minutes in advance to go through the information, either in print or on the computer; health care providers were expected to support patients to think about treatment goals and options. Patients in the control received care as usual.

Main outcome measures. The primary outcome measure was the empowerment of patients for setting and achieving goals, which was measured with the Diabetes Empowerment Scale (DES-III). Other outcome measures included changes in treatment, including intensification of drug treatment and treatment with ACE inhibitors.

Main results. A total of 344 patients were included in the study and were randomized to the intervention (n = 225) or usual care group (n = 119). Patients in the intervention group were comparable to usual care patients in terms of age, sex, and educational level. However, there were several differences between the 2 groups: intervention patients were more likely to have well-controlled HbA1c level at baseline and less likely to have well-controlled blood pressure at baseline. Among participants in the intervention group, only 46% reported to have received the basic elements of the intervention. The mean empowerment score increased 0.1 point on a 5-point scale in the intervention group, which was not different from the control group (mean adjusted difference, 0.039 points [95% confidence interval {CI}], −0.056 to 0.134). Lipid lowering medication treatment was intensified in 25% of intervention and 12% of control participants (odds ratio [OR], 2.5 [95% CI, 0.89–7.23]). Explorative analyses comparing printed version of the aid with control did find that lipid lowering medication treatment was more intensified although the confidence interval was wide (OR, 3.90 [95% CI, 1.29–11.80]). No other differences in treatment plan were observed.

Conclusions. The treatment decision aid for diabetes did not improve patient empowerment or substantially alter treatment plan when compared to usual care. However, this finding is limited by the uptake of use of the decision aid during the study period.

Commentary

Patient engagement through shared decision making is an important element in chronic disease management, particularly in diseases such as diabetes where there are a number of significant tasks, including monitoring and administration of medication, that are key to its successful management.  The use of decision aids is an innovation that has demonstrated effects in improving patient understanding of disease, and has potential downstream effect in improving management and control of the disease [1]. However, the use of decision aids is not without limitations—patients with poorer health literacy, and perhaps lower socioeconomic status, may derive less clinical benefit [2], and in older adults cognitive and physical limitations may also limit their use.

This study found that the decision aid used in the study did not significantly improve patient empowerment or alter treatment plan. In comparison with previous studies on decision aids for diabetes [3,4], this study is notable that it did not find any significant clinical impact of the decision aid when compared with usual care. However, it is important to consider reasons that may explain its null finding. First, the study has a rather complicated design, with 4 different intervention groups. The study design attempts to differentiate intervention groups with differences in its delivery (computer screen vs. printed) and content (focused information on myocardial infarction risk outcome only vs. all outcomes). The rationale was that it could provide evidence to perhaps suggest the most effective decision aid, but the drawback is that it has the potential to weaken the power of the study, increasing the likelihood of a false-negative finding. Second, in contrast to other studies, this study also uses a different measurement as its primary outcome—a measurement of patient empowerment. Though an important concept to measure, it is less clear what the expected impact and what the level of clinical significance would be. Third, as noted by the investigators, the decision aid had limited uptake in the intervention group; this may be related to its design and format. The challenge in design of a decision aid is that it needs to be simple and easy to use, consume little time, yet be adequately informative with helpful information for patients. Finally, another unique feature of the study is that the control group was an active control group, in that the providers in the practices had significant training in motivational interviewing and communication, which may have made it more challenging to demonstrate impact in intervention group.

Applications for Clinical Practice

Decision aids remain a potentially important addition for patients in the management of chronic diseases such as diabetes. Most studies have demonstrated significant impact. Despite the limitations of the current study, it does point out that different formats of decision aid may have different effects on patient outcomes. For practices that are adopting decision aids for chronic disease management, they need to take into account the format, the information, and the burden of use of the decision aid. Further studies may help to elucidate how decision aids can be optimized for maximizing clinical impact.

—William Hung, MD, MPH

Study Overview

Objective. To determine if a patient-oriented decision aid for prioritizing treatment goals in diabetes leads to changes in patient empowerment for setting and achieving goals and in treatment.

Design. Randomized controlled trial.

Setting and participants. Study participants were recruited from 18 general practices in the north of the Netherlands between April 2011 and August 2012. Participants were included if they had a diagnosis of type 2 diabetes and were managed in primary care. Participants were identified from the electronic medical record system and at least 40 patients were selected from each practice to be contacted for participation. Subjects were excluded if they had myocardial infarction in the preceding year, experienced a stroke, had heart failure, angina, or a terminal illness, or were more than 65 years of age when they received their diabetes diagnosis. Other exclusion criteria include dementia, cognitive deficits, blindness, or an inability to read Dutch. Eligibility criteria were confirmed with the health care provider from each practice. Practices that were included in the study had several features: (1) each had an electronic medical record system supporting structured care protocols; (2) most practices have a nurse practitioner or specialized assistant for diabetes care who carries out the quarterly diabetes checks and is trained to conduct physical examinations, risk assessments, patient education, and counseling; (3) all practices received training in motivational interviewing.

The decision aid format was either a computer screen or printed version, and presented as either a short version, showing treatment effects on myocardial infarction risk only, or as an extended version, including effects on additional outcomes (stroke, amputation, blindness, renal failure). Practices were randomly assigned to use the computer screen or printed version, stratified by practice size (< 2500 patients or > 2500 patients) and number of GPs (solo or several). Within each practice, consenting patients were randomized to receive the short version aid, the extended version, or to the control group.

Intervention. The decision aid presents individually tailored information on risks and treatment options for multiple risk factors. The aid focuses on shared goal setting and decision making, particularly with respect to the drug treatment of risk factors including hemoglobin A1c, systolic blood pressure, low density lipoprotein cholesterol, and smoking. The decision aid is designed to be used by patients before a regular check-up and discussed with their health care provider during a visit to help prioritize treatment that will maximize outcomes; the aid helps to summarize effects of the various treatment options. The patients were asked to come to the practice 15 minutes in advance to go through the information, either in print or on the computer; health care providers were expected to support patients to think about treatment goals and options. Patients in the control received care as usual.

Main outcome measures. The primary outcome measure was the empowerment of patients for setting and achieving goals, which was measured with the Diabetes Empowerment Scale (DES-III). Other outcome measures included changes in treatment, including intensification of drug treatment and treatment with ACE inhibitors.

Main results. A total of 344 patients were included in the study and were randomized to the intervention (n = 225) or usual care group (n = 119). Patients in the intervention group were comparable to usual care patients in terms of age, sex, and educational level. However, there were several differences between the 2 groups: intervention patients were more likely to have well-controlled HbA1c level at baseline and less likely to have well-controlled blood pressure at baseline. Among participants in the intervention group, only 46% reported to have received the basic elements of the intervention. The mean empowerment score increased 0.1 point on a 5-point scale in the intervention group, which was not different from the control group (mean adjusted difference, 0.039 points [95% confidence interval {CI}], −0.056 to 0.134). Lipid lowering medication treatment was intensified in 25% of intervention and 12% of control participants (odds ratio [OR], 2.5 [95% CI, 0.89–7.23]). Explorative analyses comparing printed version of the aid with control did find that lipid lowering medication treatment was more intensified although the confidence interval was wide (OR, 3.90 [95% CI, 1.29–11.80]). No other differences in treatment plan were observed.

Conclusions. The treatment decision aid for diabetes did not improve patient empowerment or substantially alter treatment plan when compared to usual care. However, this finding is limited by the uptake of use of the decision aid during the study period.

Commentary

Patient engagement through shared decision making is an important element in chronic disease management, particularly in diseases such as diabetes where there are a number of significant tasks, including monitoring and administration of medication, that are key to its successful management.  The use of decision aids is an innovation that has demonstrated effects in improving patient understanding of disease, and has potential downstream effect in improving management and control of the disease [1]. However, the use of decision aids is not without limitations—patients with poorer health literacy, and perhaps lower socioeconomic status, may derive less clinical benefit [2], and in older adults cognitive and physical limitations may also limit their use.

This study found that the decision aid used in the study did not significantly improve patient empowerment or alter treatment plan. In comparison with previous studies on decision aids for diabetes [3,4], this study is notable that it did not find any significant clinical impact of the decision aid when compared with usual care. However, it is important to consider reasons that may explain its null finding. First, the study has a rather complicated design, with 4 different intervention groups. The study design attempts to differentiate intervention groups with differences in its delivery (computer screen vs. printed) and content (focused information on myocardial infarction risk outcome only vs. all outcomes). The rationale was that it could provide evidence to perhaps suggest the most effective decision aid, but the drawback is that it has the potential to weaken the power of the study, increasing the likelihood of a false-negative finding. Second, in contrast to other studies, this study also uses a different measurement as its primary outcome—a measurement of patient empowerment. Though an important concept to measure, it is less clear what the expected impact and what the level of clinical significance would be. Third, as noted by the investigators, the decision aid had limited uptake in the intervention group; this may be related to its design and format. The challenge in design of a decision aid is that it needs to be simple and easy to use, consume little time, yet be adequately informative with helpful information for patients. Finally, another unique feature of the study is that the control group was an active control group, in that the providers in the practices had significant training in motivational interviewing and communication, which may have made it more challenging to demonstrate impact in intervention group.

Applications for Clinical Practice

Decision aids remain a potentially important addition for patients in the management of chronic diseases such as diabetes. Most studies have demonstrated significant impact. Despite the limitations of the current study, it does point out that different formats of decision aid may have different effects on patient outcomes. For practices that are adopting decision aids for chronic disease management, they need to take into account the format, the information, and the burden of use of the decision aid. Further studies may help to elucidate how decision aids can be optimized for maximizing clinical impact.

—William Hung, MD, MPH

Study Overview

Objective. To determine if a patient-oriented decision aid for prioritizing treatment goals in diabetes leads to changes in patient empowerment for setting and achieving goals and in treatment.

Design. Randomized controlled trial.

Setting and participants. Study participants were recruited from 18 general practices in the north of the Netherlands between April 2011 and August 2012. Participants were included if they had a diagnosis of type 2 diabetes and were managed in primary care. Participants were identified from the electronic medical record system and at least 40 patients were selected from each practice to be contacted for participation. Subjects were excluded if they had myocardial infarction in the preceding year, experienced a stroke, had heart failure, angina, or a terminal illness, or were more than 65 years of age when they received their diabetes diagnosis. Other exclusion criteria include dementia, cognitive deficits, blindness, or an inability to read Dutch. Eligibility criteria were confirmed with the health care provider from each practice. Practices that were included in the study had several features: (1) each had an electronic medical record system supporting structured care protocols; (2) most practices have a nurse practitioner or specialized assistant for diabetes care who carries out the quarterly diabetes checks and is trained to conduct physical examinations, risk assessments, patient education, and counseling; (3) all practices received training in motivational interviewing.

The decision aid format was either a computer screen or printed version, and presented as either a short version, showing treatment effects on myocardial infarction risk only, or as an extended version, including effects on additional outcomes (stroke, amputation, blindness, renal failure). Practices were randomly assigned to use the computer screen or printed version, stratified by practice size (< 2500 patients or > 2500 patients) and number of GPs (solo or several). Within each practice, consenting patients were randomized to receive the short version aid, the extended version, or to the control group.

Intervention. The decision aid presents individually tailored information on risks and treatment options for multiple risk factors. The aid focuses on shared goal setting and decision making, particularly with respect to the drug treatment of risk factors including hemoglobin A1c, systolic blood pressure, low density lipoprotein cholesterol, and smoking. The decision aid is designed to be used by patients before a regular check-up and discussed with their health care provider during a visit to help prioritize treatment that will maximize outcomes; the aid helps to summarize effects of the various treatment options. The patients were asked to come to the practice 15 minutes in advance to go through the information, either in print or on the computer; health care providers were expected to support patients to think about treatment goals and options. Patients in the control received care as usual.

Main outcome measures. The primary outcome measure was the empowerment of patients for setting and achieving goals, which was measured with the Diabetes Empowerment Scale (DES-III). Other outcome measures included changes in treatment, including intensification of drug treatment and treatment with ACE inhibitors.

Main results. A total of 344 patients were included in the study and were randomized to the intervention (n = 225) or usual care group (n = 119). Patients in the intervention group were comparable to usual care patients in terms of age, sex, and educational level. However, there were several differences between the 2 groups: intervention patients were more likely to have well-controlled HbA1c level at baseline and less likely to have well-controlled blood pressure at baseline. Among participants in the intervention group, only 46% reported to have received the basic elements of the intervention. The mean empowerment score increased 0.1 point on a 5-point scale in the intervention group, which was not different from the control group (mean adjusted difference, 0.039 points [95% confidence interval {CI}], −0.056 to 0.134). Lipid lowering medication treatment was intensified in 25% of intervention and 12% of control participants (odds ratio [OR], 2.5 [95% CI, 0.89–7.23]). Explorative analyses comparing printed version of the aid with control did find that lipid lowering medication treatment was more intensified although the confidence interval was wide (OR, 3.90 [95% CI, 1.29–11.80]). No other differences in treatment plan were observed.

Conclusions. The treatment decision aid for diabetes did not improve patient empowerment or substantially alter treatment plan when compared to usual care. However, this finding is limited by the uptake of use of the decision aid during the study period.

Commentary

Patient engagement through shared decision making is an important element in chronic disease management, particularly in diseases such as diabetes where there are a number of significant tasks, including monitoring and administration of medication, that are key to its successful management.  The use of decision aids is an innovation that has demonstrated effects in improving patient understanding of disease, and has potential downstream effect in improving management and control of the disease [1]. However, the use of decision aids is not without limitations—patients with poorer health literacy, and perhaps lower socioeconomic status, may derive less clinical benefit [2], and in older adults cognitive and physical limitations may also limit their use.

This study found that the decision aid used in the study did not significantly improve patient empowerment or alter treatment plan. In comparison with previous studies on decision aids for diabetes [3,4], this study is notable that it did not find any significant clinical impact of the decision aid when compared with usual care. However, it is important to consider reasons that may explain its null finding. First, the study has a rather complicated design, with 4 different intervention groups. The study design attempts to differentiate intervention groups with differences in its delivery (computer screen vs. printed) and content (focused information on myocardial infarction risk outcome only vs. all outcomes). The rationale was that it could provide evidence to perhaps suggest the most effective decision aid, but the drawback is that it has the potential to weaken the power of the study, increasing the likelihood of a false-negative finding. Second, in contrast to other studies, this study also uses a different measurement as its primary outcome—a measurement of patient empowerment. Though an important concept to measure, it is less clear what the expected impact and what the level of clinical significance would be. Third, as noted by the investigators, the decision aid had limited uptake in the intervention group; this may be related to its design and format. The challenge in design of a decision aid is that it needs to be simple and easy to use, consume little time, yet be adequately informative with helpful information for patients. Finally, another unique feature of the study is that the control group was an active control group, in that the providers in the practices had significant training in motivational interviewing and communication, which may have made it more challenging to demonstrate impact in intervention group.

Applications for Clinical Practice

Decision aids remain a potentially important addition for patients in the management of chronic diseases such as diabetes. Most studies have demonstrated significant impact. Despite the limitations of the current study, it does point out that different formats of decision aid may have different effects on patient outcomes. For practices that are adopting decision aids for chronic disease management, they need to take into account the format, the information, and the burden of use of the decision aid. Further studies may help to elucidate how decision aids can be optimized for maximizing clinical impact.

—William Hung, MD, MPH

Study Overview

Objective. To examine whether an early response (or non-response) to lorcaserin therapy predicts ≥ 5% weight loss achieved at 1 year.

Study design. Secondary analysis of data collected in 3 placebo-controlled blinded randomized trials.

Setting and participants. This study relied upon data collected as part of 3 separate phase 3 clinical trials of lorcaserin, a weight loss drug and selective serotonin 2c (5-HT2c) agonist. The first study, “Behavioral Modification and Lorcaserin for Overweight and Obesity Management” (BLOOM; n = 3182) enrolled overweight (with at least 1 comorbidity) or obese (no comorbidity needed) adult patients (18–65 yr) without diabetes, to determine the safety and efficacy of lorcaserin. The second trial, “Behavioral Modification and Lorcaserin Second Study of Obesity Management” (BLOSSOM; n = 4008) enrolled a similar population as BLOOM. For both BLOOM and BLOSSOM, patients were randomly assigned to receive either lorcaserin (10 mg po bid) or placebo for a 1-year period, and all patients received advice and instruction in exercise goals (at least 30 min/day) and caloric intake (600 kcal less than recommended for weight maintenance for that individual) necessary to promote weight loss. The third trial, BLOOM-DM (n = 604) focused on overweight or obese diabetic patients, but otherwise was similar in methodology to BLOOM and BLOSSOM. All studies took place in multiple US academic and private medical centers and were funded by Arena Pharmaceuticals. For the current analysis, the investigators used data from these trials and classified participants as either “responders” or “non-responders” based on each participant’s early weight loss response to either lorcaserin or placebo.

Main outcome measures. The investigators used area under the curve for the receiver operating characteristic (AUC for ROC) analysis to determine whether an early weight loss response to lorcaserin or placebo predicted a patient’s longer-term (52-week) weight loss. Several steps were used to conduct these analyses.

First, the investigators needed to determine what amount of weight loss at which of several early time-points would qualify a participant as a “responder” to either drug or placebo. They compared weight lost at weeks 2, 4, 8 and 12, using AUC for ROC analysis to identify the appropriate “responder” or “non-responder” cut-points, and classified all participants with data points in these early weeks as such. Second, all of the early responder and non-responder participants with 52-week weight data were then classified as to whether or not they had achieved at least a 5% weight loss at the end of the study. AUC for ROC analysis was again used to determine whether this early categorization was predictive of final study response. In addition to looking at early response as predictive of final weight loss, the investigators also examined the response/non-response variable’s ability to predict other health outcomes, including changes in lipid levels, blood pressure, and, for type 2 diabetic participants, changes in glycemic control (fasting plasma glucose [FPG] and HgbA1c). Finally, the investigators examined the incidence of adverse events in the different groups as well.

Results. The investigators identified a 4.6% weight loss by week 12 on lorcaserin or placebo as the optimal cut-point for determining whether a participant was a “responder” or “non-responder” (“W12R” or “W12NR”). This cut-point had an AUC (95% CI) of 0.849 (0.828–0.870) for predicting ≥ 5% weight loss at 52 weeks, with a positive predictive value (PPV) of 0.855 and negative predictive value (NPV) of 0.740, thus optimizing specificity and sensitivity of the time/weight cut-point compared to those at weeks 2, 4, or 8. Given the need for practical clinical recommendations, however, the investigators used a cut-point of 5% weight loss by week 12 to determine response/non-response for the health outcome analyses. The breakdown of responders vs. non-responders was as follows: For the pooled BLOOM/BLOSSOM participants, there were 1251 lorcaserin-recipient responders and 1286 lorcaserin-recipient non-responders (about 40% of those randomized to lorcaserin were “responders”). Among placebo recipients, there were 541 early responders and 1852 non-responders (about 17% of those randomized to placebo were “responders”). For the diabetic BLOOM-DM participants, the ratios were similar although slightly less favorable, with only about 30% (n = 78) of lorcaserin patients classified W12 responders (139 non-responders), and 10% (n = 25) of placebo patients as W12 responders (192 non-responders).

The lorcaserin and placebo groups in BLOOM and BLOSSOM were similar to one another, with overall mean (SD) age of 43.8 (11.6) years for lorcaserin and 44.0 (11.4) years for placebo. The vast majority of participants in these 2 trials were female (81.7% in lorcaserin arms, 81.0% in placebo), and the majority were non-Hispanic white (67.6% in lorcaserin and 66.2% in placebo). The mean (SD) baseline body mass index (BMI, kg/m²) was 36.1 (4.3) for lorcaserin and 36.1 (4.2) for placebo. The BLOOM-DM participants were also similar in the lorcaserin and placebo arms, although they were older (mean age, 53.2 years lorcaserin, 52 years placebo), and more likely to be female (53.5% lorcaserin, 54.4% placebo). Otherwise, the BLOOM-DM participants were similar on reported demographic characteristics to those in the other 2 trials.

Importantly, however, for all 3 trials there were differences in demographic characteristics between those participants characterized as responders and those characterized as non-responders. Amongst the nondiabetic participants in the BLOOM and BLOSSOM studies, responders (to both lorcaserin and placebo) were more likely to be non-Hispanic white (as opposed to African American or Hispanic participants, who were more likely to be non-responders), and responders were older than non-responders. Interestingly, for the diabetics in the BLOOM-DM trial, the responder/non-responder differences were less pronounced, although the responders were still slightly more likely to be non-Hispanic white and older, particularly for placebo.

Among BLOOM and BLOSSOM participants who received lorcaserin, mean weight loss at 52 weeks was 10.8% among W12Rs and only 2.7% among W12NRs. A similar pattern was observed in the BLOOM and BLOSSOM placebo participants; W12Rs averaged 9.5% weight loss at 52 weeks, versus just 1.1% in W12NRs.  Among diabetics receiving lorcaserin in the BLOOM-DM study, weight loss at 1 year was 9.1% in W12Rs versus 3.1% in W12NRs. Similarly, in placebo-recipients in BLOOM-DM, weight loss at 1 year was 7% for W12Rs and 1.3% for W12NRs. When the weight loss at 1 year was categorized in terms of whether or not participants achieved at least 5% or 10% weight loss, once again early responders to either lorcaserin or placebo had higher rates of achieving both thresholds. Namely, 85.5% of nondiabetic W12Rs had achieved or maintained 5% weight loss at week 52, while only 26% of the W12NRs ultimately did so. Seventy percent of diabetic W12Rs to lorcaserin had ≥ 5% weight loss at week 52 and 25.2% of W12NRs did. The pattern of prediction for achieving 10% weight loss at week 52 was even more pronounced, with, for example, 49.8% of nondiabetic W12Rs having lost at least 10% of their starting weight at 1 year, versus just 4.7% of W12NRs.

When cardiometabolic outcomes were examined, the differences between W12 lorcaserin responders and non-responders appeared to be somewhat attenuated. For example, among diabetic patients, W12 lorcaserin responders had a mean decrease of 1.2% in their A1c level by study end, compared to a nearly 1% decrease in W12NRs. For fasting plasma glucose, the improvement at week 52 was pronounced (about 30 mg/dL lower than baseline) and very similar in W12 responders and non-responders.

Among nondiabetics, average blood pressure lowering (SBP and DBP) at week 52 was greater among lorcaserin W12 responders (SBP dropped 4 mm Hg on average, DBP 3 mm Hg) than it was among non-responders (SBP and DBP dropped by about 1 mm Hg). Other than triglycerides, which decreased substantially among W12 responders (whether on placebo or lorcaserin), changes to lipid profile were relatively small for nondiabetics. Among diabetics, however, LDL and HDL both increased on average in all 4 groups (W12 responders/non-responders to placebo/lorcaserin) by week 52.

Common adverse events for lorcaserin-treated patients included headache (15%–17%), upper respiratory infections (9%–14%), nausea (8%–9%), and dizziness (8% among nondiabetics). Among diabetics, hypoglycemia occurred in 29.3% of those treated with lorcaserin (vs. 21% on placebo). Week 12 responders and non-responders appeared to have a similar adverse event profile, and, in general, adverse events were more common among lorcaserin than placebo participants.

Conclusion. The authors of this study concluded that a week-12 weight loss of ≥ 5% on lorcaserin was a strong predictor of achieving at least that same amount of weight loss, as well as improvements in some cardiometabolic parameters, at 1 year.

Commentary

In 2013, the American Medical Association officially recognized obesity as a disease. This shift in terminology, coupled with a movement towards reimbursing primary care providers for obesity-related interventions, has created a growing awareness among providers that better treatment options for this chronic condition are sorely needed. Just as we treat patients with hypertension and type 2 diabetes by titrating medications, discontinuing those that aren’t effective and continuing those that are, so should we approach the management of our patients with obesity. Although behavioral interventions centered around lifestyle changes (diet/exercise) remain first-line therapies for the treatment of obesity [1], many patients will seek additional tools, such as meal replacement, medication, or even bariatric surgery, to help achieve and maintain weight loss.

In the past 2 to 3 years, there has been a flurry of activity by the FDA to approve new medications for weight loss. In keeping with the view of obesity as a chronic condition, some of these medications, including lorcaserin and phentermine-topiramate ER, have even been approved for patient long-term use [2]. While the addition of new options to the weight loss toolkit is exciting, it may also be daunting for clinicians who have witnessed a bevy of weight loss drugs come on, and then off, the market over the years due to serious adverse events experienced by patients. For physicians and patients considering the use of a new weight loss medication, there is therefore a clear need to minimize risk for adverse effects related to the drug, while maximizing the patient’s chances of losing weight.

Growing evidence from trials of behavioral interventions as well as weight loss medications suggests that the individuals who will ultimately achieve weight loss success with a given intervention/medication, tend to indicate that success relatively early on in the course of therapy [3–5]. For clinicians, this fact is extremely useful, because it may allow the physician and patient to more rapidly make a decision to discontinue a likely ineffective option in favor of another that has not yet been tried, thus minimizing risks for adverse events while maximizing chances of weight loss outcomes.

In this paper, Smith and colleagues addressed this very important issue for one of the more recently FDA-approved medications, lorcaserin. This 5-HT2c agonist is a useful addition to the list of weight loss medications, as it has relatively few contraindications, other than that it cannot be used in pregnancy/lactation and should be avoided in those with a history of heart failure. However, lorcaserin is still relatively costly (eg, compared to phentermine) and, if it is going to be used for long-term weight loss/maintenance, the financial outlay faced by patients might be considerable. In addition to answering an important question, this paper also examined not only weight loss outcomes but also cardiometabolic impacts of the medication. Furthermore, the authors separately examined outcomes for diabetic and nondiabetic patients, as the risk/benefit ratio of remaining on this medication could be quite different between the 2 groups.

Importantly, the study represented a group of secondary analyses of data aggregated from several trials—trials that were not originally designed to answer this question. Although the majority of original trial participants did have data at weeks 12 and 52 (requirement for inclusion in this analysis), up to a quarter of patients in some groups were missing one or the other measure. Whether or not those analyzed represented a biased subsample, and therefore do not have generalizable results, cannot be ascertained.

In reviewing the outcomes achieved by early responders and non-responders, it was very interesting to note that so-called “responders” to placebo followed a nearly identical weight loss trajectory as those on lorcaserin. This fact should not be taken to indicate that lorcaserin is no different from placebo, as the overall chances of achieving weight loss were significantly greater among the lorcaserin participants. However, it is interesting that, for those placebo patients who clearly followed the recommended lifestyle changes, they did just as well as patients receiving active study drug. This underscores the need to educate patients and encourage them, first and foremost, to make a real effort to diet and exercise regardless of what other tools are employed to achieve weight loss.

Another issue to consider for this study is that there are clear differences in the racial/ethnic makeup of responders versus non-responders. This finding is not unexpected, as in many prior weight loss trials, particularly for behavioral interventions, African-American women have experienced less weight loss than their non-Hispanic white counterparts [6]. These differences were observed both for lorcaserin and placebo patients, raising a concern that the lifestyle intervention component of the study was not equally successful for minorities compared to the non-Hispanic white participants. More research is needed on behavioral interventions that work well in diverse populations.

One finding of interest is that among diabetic participants (BLOOM-DM), glycemic control parameters improved nearly equally between lorcaserin early responders and non-responders, despite the differences between those groups for year-end weight loss. The reasons for this are not clear but could merit further investigation.

Ultimately however, even among this large group of randomized trial participants, who were likely highly motivated, only about 40% of nondiabetics and 30% of diabetics were classified as week 12 responders to lorcaserin. That means that likely well over half of the real-world patients who initiate the drug may not achieve their desired weight loss goals with it. Given the cost of the medication, this must be considered before prescribing it, and it reinforces the importance of being willing to reassess a patient’s weight loss progress early and often so that the medication can be discontinued in favor of other therapies as needed.

Applications for Clinical Practice

For providers interested in prescribing lorcaserin to their patients, a clear plan should be made to have regular and early follow-up to assess the patient’s response to the medication. Patients should understand that if they are not responding to the medication within 3 months, or perhaps sooner if they are experiencing any negative side effects, their physician may elect to discontinue it. Importantly, they should only be given lorcaserin if they are also willing to undertake the behavioral changes necessary to promote weight loss, and it should be underscored that their chances of successful weight loss with or without the medication will be greatly enhanced by doing so.

 —Kristina Lewis, MD, MPH

Study Overview

Objective. To examine whether an early response (or non-response) to lorcaserin therapy predicts ≥ 5% weight loss achieved at 1 year.

Study design. Secondary analysis of data collected in 3 placebo-controlled blinded randomized trials.

Setting and participants. This study relied upon data collected as part of 3 separate phase 3 clinical trials of lorcaserin, a weight loss drug and selective serotonin 2c (5-HT2c) agonist. The first study, “Behavioral Modification and Lorcaserin for Overweight and Obesity Management” (BLOOM; n = 3182) enrolled overweight (with at least 1 comorbidity) or obese (no comorbidity needed) adult patients (18–65 yr) without diabetes, to determine the safety and efficacy of lorcaserin. The second trial, “Behavioral Modification and Lorcaserin Second Study of Obesity Management” (BLOSSOM; n = 4008) enrolled a similar population as BLOOM. For both BLOOM and BLOSSOM, patients were randomly assigned to receive either lorcaserin (10 mg po bid) or placebo for a 1-year period, and all patients received advice and instruction in exercise goals (at least 30 min/day) and caloric intake (600 kcal less than recommended for weight maintenance for that individual) necessary to promote weight loss. The third trial, BLOOM-DM (n = 604) focused on overweight or obese diabetic patients, but otherwise was similar in methodology to BLOOM and BLOSSOM. All studies took place in multiple US academic and private medical centers and were funded by Arena Pharmaceuticals. For the current analysis, the investigators used data from these trials and classified participants as either “responders” or “non-responders” based on each participant’s early weight loss response to either lorcaserin or placebo.

Main outcome measures. The investigators used area under the curve for the receiver operating characteristic (AUC for ROC) analysis to determine whether an early weight loss response to lorcaserin or placebo predicted a patient’s longer-term (52-week) weight loss. Several steps were used to conduct these analyses.

First, the investigators needed to determine what amount of weight loss at which of several early time-points would qualify a participant as a “responder” to either drug or placebo. They compared weight lost at weeks 2, 4, 8 and 12, using AUC for ROC analysis to identify the appropriate “responder” or “non-responder” cut-points, and classified all participants with data points in these early weeks as such. Second, all of the early responder and non-responder participants with 52-week weight data were then classified as to whether or not they had achieved at least a 5% weight loss at the end of the study. AUC for ROC analysis was again used to determine whether this early categorization was predictive of final study response. In addition to looking at early response as predictive of final weight loss, the investigators also examined the response/non-response variable’s ability to predict other health outcomes, including changes in lipid levels, blood pressure, and, for type 2 diabetic participants, changes in glycemic control (fasting plasma glucose [FPG] and HgbA1c). Finally, the investigators examined the incidence of adverse events in the different groups as well.

Results. The investigators identified a 4.6% weight loss by week 12 on lorcaserin or placebo as the optimal cut-point for determining whether a participant was a “responder” or “non-responder” (“W12R” or “W12NR”). This cut-point had an AUC (95% CI) of 0.849 (0.828–0.870) for predicting ≥ 5% weight loss at 52 weeks, with a positive predictive value (PPV) of 0.855 and negative predictive value (NPV) of 0.740, thus optimizing specificity and sensitivity of the time/weight cut-point compared to those at weeks 2, 4, or 8. Given the need for practical clinical recommendations, however, the investigators used a cut-point of 5% weight loss by week 12 to determine response/non-response for the health outcome analyses. The breakdown of responders vs. non-responders was as follows: For the pooled BLOOM/BLOSSOM participants, there were 1251 lorcaserin-recipient responders and 1286 lorcaserin-recipient non-responders (about 40% of those randomized to lorcaserin were “responders”). Among placebo recipients, there were 541 early responders and 1852 non-responders (about 17% of those randomized to placebo were “responders”). For the diabetic BLOOM-DM participants, the ratios were similar although slightly less favorable, with only about 30% (n = 78) of lorcaserin patients classified W12 responders (139 non-responders), and 10% (n = 25) of placebo patients as W12 responders (192 non-responders).

The lorcaserin and placebo groups in BLOOM and BLOSSOM were similar to one another, with overall mean (SD) age of 43.8 (11.6) years for lorcaserin and 44.0 (11.4) years for placebo. The vast majority of participants in these 2 trials were female (81.7% in lorcaserin arms, 81.0% in placebo), and the majority were non-Hispanic white (67.6% in lorcaserin and 66.2% in placebo). The mean (SD) baseline body mass index (BMI, kg/m²) was 36.1 (4.3) for lorcaserin and 36.1 (4.2) for placebo. The BLOOM-DM participants were also similar in the lorcaserin and placebo arms, although they were older (mean age, 53.2 years lorcaserin, 52 years placebo), and more likely to be female (53.5% lorcaserin, 54.4% placebo). Otherwise, the BLOOM-DM participants were similar on reported demographic characteristics to those in the other 2 trials.

Importantly, however, for all 3 trials there were differences in demographic characteristics between those participants characterized as responders and those characterized as non-responders. Amongst the nondiabetic participants in the BLOOM and BLOSSOM studies, responders (to both lorcaserin and placebo) were more likely to be non-Hispanic white (as opposed to African American or Hispanic participants, who were more likely to be non-responders), and responders were older than non-responders. Interestingly, for the diabetics in the BLOOM-DM trial, the responder/non-responder differences were less pronounced, although the responders were still slightly more likely to be non-Hispanic white and older, particularly for placebo.

Among BLOOM and BLOSSOM participants who received lorcaserin, mean weight loss at 52 weeks was 10.8% among W12Rs and only 2.7% among W12NRs. A similar pattern was observed in the BLOOM and BLOSSOM placebo participants; W12Rs averaged 9.5% weight loss at 52 weeks, versus just 1.1% in W12NRs.  Among diabetics receiving lorcaserin in the BLOOM-DM study, weight loss at 1 year was 9.1% in W12Rs versus 3.1% in W12NRs. Similarly, in placebo-recipients in BLOOM-DM, weight loss at 1 year was 7% for W12Rs and 1.3% for W12NRs. When the weight loss at 1 year was categorized in terms of whether or not participants achieved at least 5% or 10% weight loss, once again early responders to either lorcaserin or placebo had higher rates of achieving both thresholds. Namely, 85.5% of nondiabetic W12Rs had achieved or maintained 5% weight loss at week 52, while only 26% of the W12NRs ultimately did so. Seventy percent of diabetic W12Rs to lorcaserin had ≥ 5% weight loss at week 52 and 25.2% of W12NRs did. The pattern of prediction for achieving 10% weight loss at week 52 was even more pronounced, with, for example, 49.8% of nondiabetic W12Rs having lost at least 10% of their starting weight at 1 year, versus just 4.7% of W12NRs.

When cardiometabolic outcomes were examined, the differences between W12 lorcaserin responders and non-responders appeared to be somewhat attenuated. For example, among diabetic patients, W12 lorcaserin responders had a mean decrease of 1.2% in their A1c level by study end, compared to a nearly 1% decrease in W12NRs. For fasting plasma glucose, the improvement at week 52 was pronounced (about 30 mg/dL lower than baseline) and very similar in W12 responders and non-responders.

Among nondiabetics, average blood pressure lowering (SBP and DBP) at week 52 was greater among lorcaserin W12 responders (SBP dropped 4 mm Hg on average, DBP 3 mm Hg) than it was among non-responders (SBP and DBP dropped by about 1 mm Hg). Other than triglycerides, which decreased substantially among W12 responders (whether on placebo or lorcaserin), changes to lipid profile were relatively small for nondiabetics. Among diabetics, however, LDL and HDL both increased on average in all 4 groups (W12 responders/non-responders to placebo/lorcaserin) by week 52.

Common adverse events for lorcaserin-treated patients included headache (15%–17%), upper respiratory infections (9%–14%), nausea (8%–9%), and dizziness (8% among nondiabetics). Among diabetics, hypoglycemia occurred in 29.3% of those treated with lorcaserin (vs. 21% on placebo). Week 12 responders and non-responders appeared to have a similar adverse event profile, and, in general, adverse events were more common among lorcaserin than placebo participants.

Conclusion. The authors of this study concluded that a week-12 weight loss of ≥ 5% on lorcaserin was a strong predictor of achieving at least that same amount of weight loss, as well as improvements in some cardiometabolic parameters, at 1 year.

Commentary

In 2013, the American Medical Association officially recognized obesity as a disease. This shift in terminology, coupled with a movement towards reimbursing primary care providers for obesity-related interventions, has created a growing awareness among providers that better treatment options for this chronic condition are sorely needed. Just as we treat patients with hypertension and type 2 diabetes by titrating medications, discontinuing those that aren’t effective and continuing those that are, so should we approach the management of our patients with obesity. Although behavioral interventions centered around lifestyle changes (diet/exercise) remain first-line therapies for the treatment of obesity [1], many patients will seek additional tools, such as meal replacement, medication, or even bariatric surgery, to help achieve and maintain weight loss.

In the past 2 to 3 years, there has been a flurry of activity by the FDA to approve new medications for weight loss. In keeping with the view of obesity as a chronic condition, some of these medications, including lorcaserin and phentermine-topiramate ER, have even been approved for patient long-term use [2]. While the addition of new options to the weight loss toolkit is exciting, it may also be daunting for clinicians who have witnessed a bevy of weight loss drugs come on, and then off, the market over the years due to serious adverse events experienced by patients. For physicians and patients considering the use of a new weight loss medication, there is therefore a clear need to minimize risk for adverse effects related to the drug, while maximizing the patient’s chances of losing weight.

Growing evidence from trials of behavioral interventions as well as weight loss medications suggests that the individuals who will ultimately achieve weight loss success with a given intervention/medication, tend to indicate that success relatively early on in the course of therapy [3–5]. For clinicians, this fact is extremely useful, because it may allow the physician and patient to more rapidly make a decision to discontinue a likely ineffective option in favor of another that has not yet been tried, thus minimizing risks for adverse events while maximizing chances of weight loss outcomes.

In this paper, Smith and colleagues addressed this very important issue for one of the more recently FDA-approved medications, lorcaserin. This 5-HT2c agonist is a useful addition to the list of weight loss medications, as it has relatively few contraindications, other than that it cannot be used in pregnancy/lactation and should be avoided in those with a history of heart failure. However, lorcaserin is still relatively costly (eg, compared to phentermine) and, if it is going to be used for long-term weight loss/maintenance, the financial outlay faced by patients might be considerable. In addition to answering an important question, this paper also examined not only weight loss outcomes but also cardiometabolic impacts of the medication. Furthermore, the authors separately examined outcomes for diabetic and nondiabetic patients, as the risk/benefit ratio of remaining on this medication could be quite different between the 2 groups.

Importantly, the study represented a group of secondary analyses of data aggregated from several trials—trials that were not originally designed to answer this question. Although the majority of original trial participants did have data at weeks 12 and 52 (requirement for inclusion in this analysis), up to a quarter of patients in some groups were missing one or the other measure. Whether or not those analyzed represented a biased subsample, and therefore do not have generalizable results, cannot be ascertained.

In reviewing the outcomes achieved by early responders and non-responders, it was very interesting to note that so-called “responders” to placebo followed a nearly identical weight loss trajectory as those on lorcaserin. This fact should not be taken to indicate that lorcaserin is no different from placebo, as the overall chances of achieving weight loss were significantly greater among the lorcaserin participants. However, it is interesting that, for those placebo patients who clearly followed the recommended lifestyle changes, they did just as well as patients receiving active study drug. This underscores the need to educate patients and encourage them, first and foremost, to make a real effort to diet and exercise regardless of what other tools are employed to achieve weight loss.

Another issue to consider for this study is that there are clear differences in the racial/ethnic makeup of responders versus non-responders. This finding is not unexpected, as in many prior weight loss trials, particularly for behavioral interventions, African-American women have experienced less weight loss than their non-Hispanic white counterparts [6]. These differences were observed both for lorcaserin and placebo patients, raising a concern that the lifestyle intervention component of the study was not equally successful for minorities compared to the non-Hispanic white participants. More research is needed on behavioral interventions that work well in diverse populations.

One finding of interest is that among diabetic participants (BLOOM-DM), glycemic control parameters improved nearly equally between lorcaserin early responders and non-responders, despite the differences between those groups for year-end weight loss. The reasons for this are not clear but could merit further investigation.

Ultimately however, even among this large group of randomized trial participants, who were likely highly motivated, only about 40% of nondiabetics and 30% of diabetics were classified as week 12 responders to lorcaserin. That means that likely well over half of the real-world patients who initiate the drug may not achieve their desired weight loss goals with it. Given the cost of the medication, this must be considered before prescribing it, and it reinforces the importance of being willing to reassess a patient’s weight loss progress early and often so that the medication can be discontinued in favor of other therapies as needed.

Applications for Clinical Practice

For providers interested in prescribing lorcaserin to their patients, a clear plan should be made to have regular and early follow-up to assess the patient’s response to the medication. Patients should understand that if they are not responding to the medication within 3 months, or perhaps sooner if they are experiencing any negative side effects, their physician may elect to discontinue it. Importantly, they should only be given lorcaserin if they are also willing to undertake the behavioral changes necessary to promote weight loss, and it should be underscored that their chances of successful weight loss with or without the medication will be greatly enhanced by doing so.

 —Kristina Lewis, MD, MPH

Study Overview

Objective. To examine whether an early response (or non-response) to lorcaserin therapy predicts ≥ 5% weight loss achieved at 1 year.

Study design. Secondary analysis of data collected in 3 placebo-controlled blinded randomized trials.

Setting and participants. This study relied upon data collected as part of 3 separate phase 3 clinical trials of lorcaserin, a weight loss drug and selective serotonin 2c (5-HT2c) agonist. The first study, “Behavioral Modification and Lorcaserin for Overweight and Obesity Management” (BLOOM; n = 3182) enrolled overweight (with at least 1 comorbidity) or obese (no comorbidity needed) adult patients (18–65 yr) without diabetes, to determine the safety and efficacy of lorcaserin. The second trial, “Behavioral Modification and Lorcaserin Second Study of Obesity Management” (BLOSSOM; n = 4008) enrolled a similar population as BLOOM. For both BLOOM and BLOSSOM, patients were randomly assigned to receive either lorcaserin (10 mg po bid) or placebo for a 1-year period, and all patients received advice and instruction in exercise goals (at least 30 min/day) and caloric intake (600 kcal less than recommended for weight maintenance for that individual) necessary to promote weight loss. The third trial, BLOOM-DM (n = 604) focused on overweight or obese diabetic patients, but otherwise was similar in methodology to BLOOM and BLOSSOM. All studies took place in multiple US academic and private medical centers and were funded by Arena Pharmaceuticals. For the current analysis, the investigators used data from these trials and classified participants as either “responders” or “non-responders” based on each participant’s early weight loss response to either lorcaserin or placebo.

Main outcome measures. The investigators used area under the curve for the receiver operating characteristic (AUC for ROC) analysis to determine whether an early weight loss response to lorcaserin or placebo predicted a patient’s longer-term (52-week) weight loss. Several steps were used to conduct these analyses.

First, the investigators needed to determine what amount of weight loss at which of several early time-points would qualify a participant as a “responder” to either drug or placebo. They compared weight lost at weeks 2, 4, 8 and 12, using AUC for ROC analysis to identify the appropriate “responder” or “non-responder” cut-points, and classified all participants with data points in these early weeks as such. Second, all of the early responder and non-responder participants with 52-week weight data were then classified as to whether or not they had achieved at least a 5% weight loss at the end of the study. AUC for ROC analysis was again used to determine whether this early categorization was predictive of final study response. In addition to looking at early response as predictive of final weight loss, the investigators also examined the response/non-response variable’s ability to predict other health outcomes, including changes in lipid levels, blood pressure, and, for type 2 diabetic participants, changes in glycemic control (fasting plasma glucose [FPG] and HgbA1c). Finally, the investigators examined the incidence of adverse events in the different groups as well.

Results. The investigators identified a 4.6% weight loss by week 12 on lorcaserin or placebo as the optimal cut-point for determining whether a participant was a “responder” or “non-responder” (“W12R” or “W12NR”). This cut-point had an AUC (95% CI) of 0.849 (0.828–0.870) for predicting ≥ 5% weight loss at 52 weeks, with a positive predictive value (PPV) of 0.855 and negative predictive value (NPV) of 0.740, thus optimizing specificity and sensitivity of the time/weight cut-point compared to those at weeks 2, 4, or 8. Given the need for practical clinical recommendations, however, the investigators used a cut-point of 5% weight loss by week 12 to determine response/non-response for the health outcome analyses. The breakdown of responders vs. non-responders was as follows: For the pooled BLOOM/BLOSSOM participants, there were 1251 lorcaserin-recipient responders and 1286 lorcaserin-recipient non-responders (about 40% of those randomized to lorcaserin were “responders”). Among placebo recipients, there were 541 early responders and 1852 non-responders (about 17% of those randomized to placebo were “responders”). For the diabetic BLOOM-DM participants, the ratios were similar although slightly less favorable, with only about 30% (n = 78) of lorcaserin patients classified W12 responders (139 non-responders), and 10% (n = 25) of placebo patients as W12 responders (192 non-responders).

The lorcaserin and placebo groups in BLOOM and BLOSSOM were similar to one another, with overall mean (SD) age of 43.8 (11.6) years for lorcaserin and 44.0 (11.4) years for placebo. The vast majority of participants in these 2 trials were female (81.7% in lorcaserin arms, 81.0% in placebo), and the majority were non-Hispanic white (67.6% in lorcaserin and 66.2% in placebo). The mean (SD) baseline body mass index (BMI, kg/m²) was 36.1 (4.3) for lorcaserin and 36.1 (4.2) for placebo. The BLOOM-DM participants were also similar in the lorcaserin and placebo arms, although they were older (mean age, 53.2 years lorcaserin, 52 years placebo), and more likely to be female (53.5% lorcaserin, 54.4% placebo). Otherwise, the BLOOM-DM participants were similar on reported demographic characteristics to those in the other 2 trials.

Importantly, however, for all 3 trials there were differences in demographic characteristics between those participants characterized as responders and those characterized as non-responders. Amongst the nondiabetic participants in the BLOOM and BLOSSOM studies, responders (to both lorcaserin and placebo) were more likely to be non-Hispanic white (as opposed to African American or Hispanic participants, who were more likely to be non-responders), and responders were older than non-responders. Interestingly, for the diabetics in the BLOOM-DM trial, the responder/non-responder differences were less pronounced, although the responders were still slightly more likely to be non-Hispanic white and older, particularly for placebo.

Among BLOOM and BLOSSOM participants who received lorcaserin, mean weight loss at 52 weeks was 10.8% among W12Rs and only 2.7% among W12NRs. A similar pattern was observed in the BLOOM and BLOSSOM placebo participants; W12Rs averaged 9.5% weight loss at 52 weeks, versus just 1.1% in W12NRs.  Among diabetics receiving lorcaserin in the BLOOM-DM study, weight loss at 1 year was 9.1% in W12Rs versus 3.1% in W12NRs. Similarly, in placebo-recipients in BLOOM-DM, weight loss at 1 year was 7% for W12Rs and 1.3% for W12NRs. When the weight loss at 1 year was categorized in terms of whether or not participants achieved at least 5% or 10% weight loss, once again early responders to either lorcaserin or placebo had higher rates of achieving both thresholds. Namely, 85.5% of nondiabetic W12Rs had achieved or maintained 5% weight loss at week 52, while only 26% of the W12NRs ultimately did so. Seventy percent of diabetic W12Rs to lorcaserin had ≥ 5% weight loss at week 52 and 25.2% of W12NRs did. The pattern of prediction for achieving 10% weight loss at week 52 was even more pronounced, with, for example, 49.8% of nondiabetic W12Rs having lost at least 10% of their starting weight at 1 year, versus just 4.7% of W12NRs.

When cardiometabolic outcomes were examined, the differences between W12 lorcaserin responders and non-responders appeared to be somewhat attenuated. For example, among diabetic patients, W12 lorcaserin responders had a mean decrease of 1.2% in their A1c level by study end, compared to a nearly 1% decrease in W12NRs. For fasting plasma glucose, the improvement at week 52 was pronounced (about 30 mg/dL lower than baseline) and very similar in W12 responders and non-responders.

Among nondiabetics, average blood pressure lowering (SBP and DBP) at week 52 was greater among lorcaserin W12 responders (SBP dropped 4 mm Hg on average, DBP 3 mm Hg) than it was among non-responders (SBP and DBP dropped by about 1 mm Hg). Other than triglycerides, which decreased substantially among W12 responders (whether on placebo or lorcaserin), changes to lipid profile were relatively small for nondiabetics. Among diabetics, however, LDL and HDL both increased on average in all 4 groups (W12 responders/non-responders to placebo/lorcaserin) by week 52.

Common adverse events for lorcaserin-treated patients included headache (15%–17%), upper respiratory infections (9%–14%), nausea (8%–9%), and dizziness (8% among nondiabetics). Among diabetics, hypoglycemia occurred in 29.3% of those treated with lorcaserin (vs. 21% on placebo). Week 12 responders and non-responders appeared to have a similar adverse event profile, and, in general, adverse events were more common among lorcaserin than placebo participants.

Conclusion. The authors of this study concluded that a week-12 weight loss of ≥ 5% on lorcaserin was a strong predictor of achieving at least that same amount of weight loss, as well as improvements in some cardiometabolic parameters, at 1 year.

Commentary

In 2013, the American Medical Association officially recognized obesity as a disease. This shift in terminology, coupled with a movement towards reimbursing primary care providers for obesity-related interventions, has created a growing awareness among providers that better treatment options for this chronic condition are sorely needed. Just as we treat patients with hypertension and type 2 diabetes by titrating medications, discontinuing those that aren’t effective and continuing those that are, so should we approach the management of our patients with obesity. Although behavioral interventions centered around lifestyle changes (diet/exercise) remain first-line therapies for the treatment of obesity [1], many patients will seek additional tools, such as meal replacement, medication, or even bariatric surgery, to help achieve and maintain weight loss.

In the past 2 to 3 years, there has been a flurry of activity by the FDA to approve new medications for weight loss. In keeping with the view of obesity as a chronic condition, some of these medications, including lorcaserin and phentermine-topiramate ER, have even been approved for patient long-term use [2]. While the addition of new options to the weight loss toolkit is exciting, it may also be daunting for clinicians who have witnessed a bevy of weight loss drugs come on, and then off, the market over the years due to serious adverse events experienced by patients. For physicians and patients considering the use of a new weight loss medication, there is therefore a clear need to minimize risk for adverse effects related to the drug, while maximizing the patient’s chances of losing weight.

Growing evidence from trials of behavioral interventions as well as weight loss medications suggests that the individuals who will ultimately achieve weight loss success with a given intervention/medication, tend to indicate that success relatively early on in the course of therapy [3–5]. For clinicians, this fact is extremely useful, because it may allow the physician and patient to more rapidly make a decision to discontinue a likely ineffective option in favor of another that has not yet been tried, thus minimizing risks for adverse events while maximizing chances of weight loss outcomes.

In this paper, Smith and colleagues addressed this very important issue for one of the more recently FDA-approved medications, lorcaserin. This 5-HT2c agonist is a useful addition to the list of weight loss medications, as it has relatively few contraindications, other than that it cannot be used in pregnancy/lactation and should be avoided in those with a history of heart failure. However, lorcaserin is still relatively costly (eg, compared to phentermine) and, if it is going to be used for long-term weight loss/maintenance, the financial outlay faced by patients might be considerable. In addition to answering an important question, this paper also examined not only weight loss outcomes but also cardiometabolic impacts of the medication. Furthermore, the authors separately examined outcomes for diabetic and nondiabetic patients, as the risk/benefit ratio of remaining on this medication could be quite different between the 2 groups.

Importantly, the study represented a group of secondary analyses of data aggregated from several trials—trials that were not originally designed to answer this question. Although the majority of original trial participants did have data at weeks 12 and 52 (requirement for inclusion in this analysis), up to a quarter of patients in some groups were missing one or the other measure. Whether or not those analyzed represented a biased subsample, and therefore do not have generalizable results, cannot be ascertained.

In reviewing the outcomes achieved by early responders and non-responders, it was very interesting to note that so-called “responders” to placebo followed a nearly identical weight loss trajectory as those on lorcaserin. This fact should not be taken to indicate that lorcaserin is no different from placebo, as the overall chances of achieving weight loss were significantly greater among the lorcaserin participants. However, it is interesting that, for those placebo patients who clearly followed the recommended lifestyle changes, they did just as well as patients receiving active study drug. This underscores the need to educate patients and encourage them, first and foremost, to make a real effort to diet and exercise regardless of what other tools are employed to achieve weight loss.

Another issue to consider for this study is that there are clear differences in the racial/ethnic makeup of responders versus non-responders. This finding is not unexpected, as in many prior weight loss trials, particularly for behavioral interventions, African-American women have experienced less weight loss than their non-Hispanic white counterparts [6]. These differences were observed both for lorcaserin and placebo patients, raising a concern that the lifestyle intervention component of the study was not equally successful for minorities compared to the non-Hispanic white participants. More research is needed on behavioral interventions that work well in diverse populations.

One finding of interest is that among diabetic participants (BLOOM-DM), glycemic control parameters improved nearly equally between lorcaserin early responders and non-responders, despite the differences between those groups for year-end weight loss. The reasons for this are not clear but could merit further investigation.

Ultimately however, even among this large group of randomized trial participants, who were likely highly motivated, only about 40% of nondiabetics and 30% of diabetics were classified as week 12 responders to lorcaserin. That means that likely well over half of the real-world patients who initiate the drug may not achieve their desired weight loss goals with it. Given the cost of the medication, this must be considered before prescribing it, and it reinforces the importance of being willing to reassess a patient’s weight loss progress early and often so that the medication can be discontinued in favor of other therapies as needed.

Applications for Clinical Practice

For providers interested in prescribing lorcaserin to their patients, a clear plan should be made to have regular and early follow-up to assess the patient’s response to the medication. Patients should understand that if they are not responding to the medication within 3 months, or perhaps sooner if they are experiencing any negative side effects, their physician may elect to discontinue it. Importantly, they should only be given lorcaserin if they are also willing to undertake the behavioral changes necessary to promote weight loss, and it should be underscored that their chances of successful weight loss with or without the medication will be greatly enhanced by doing so.

 —Kristina Lewis, MD, MPH

Study Overview

Objective. To determine the effectiveness of adult auditory rehabilitation programs that focus on the use of communication strategies.

Design. Nonsystematic review of the literature.

Methods. The authors used the PubMed database to search for systematic reviews investigating the effectiveness of auditory training and auditory rehabilitation programs. Auditory training involves the patient participating in a program of training designed to enhance speech perception. Training is typically provided on a repeated basis over a number of sessions and involves practice with listening and recognition of speech-based material. Auditory rehabilitation programs can be delivered to groups or individuals and usually have a focus on supplementing information about hearing loss and hearing aids with advice and/or practice with communication strategies and the management of psychosocial aspects of hearing loss.

Main outcome measures. A variety of outcomes were measured in the individual studies.

Results. One systematic review of individual auditory training and 2 systematic reviews of group auditory rehabilitation were identified. Sweetow and Palmer [1] found that auditory training in which speech was broken down into its parts was associated with improvements in the perception of speech in the presence of background noise and better use of active listening strategies. With regard to rehabilitation programs, Hawkins [2] found evidence of short-term reduction in perceived hearing handicap but less evidence of effectiveness for other outcomes. A more recent review by Chisolm and Arnold [3] included only randomized studies that examined the effect of the program on social participation and quality of life. References for the included studies were given but the summary results of this review were not included in this paper.

Conclusion. The authors conclude that there is some evidence that auditory rehabilitation programs are associated with improvements in social participation and quality of life but they acknowledge that more evidence is required.

Commentary

Adult acquired hearing loss is a common long-term condition which in the majority of cases is not remediable by surgical or medical intervention. It is the second leading cause of years lived with a disability [4]. Intervention options for people with hearing loss include hearing aid fitting and/or participation in a rehabilitative program that might include information about hearing and communication and practice or experience with listening or communication tasks [5,6]. Poorly managed hearing loss is associated with negative consequences including depression and cognitive decline [7,8]. Identifying effective management options for hearing loss can reduce these consequences and improve quality of life. In the current paper, Cardemil et al review the evidence for the effectiveness of adult auditory rehabilitation. They note that hearing aids alone cannot ameliorate all of the difficulties caused by age-related hearing loss, where cognitive factors play a significant role. They provide a rationale for why communication-based training in auditory rehabilitation has been recommended as a substitute or supplement to hearing aid fitting. That is, such training addresses the cognitive and communication difficulties that exist in addition to the hearing loss.

This paper summarises the findings of 3 systematic reviews: one on individual auditory rehabilitation and 2 on group rehabilitation. There was heterogeneity among the programs and the methods used to assess their effectiveness were variable. The reviewers conclude that there appears to be some short-term benefit to auditory rehabilitation programs but effect sizes, where effects are seen, are small and more research is needed to establish the effectiveness of these programs.

A limitation of this review is that it was nonsystematic and did not critically appraise the quality of the included systematic reviews. One weakness of the systematic reviews presented is that they did not consider interactions between content and delivery of interventions and comparisons. Individual auditory rehabilitation and group rehabilitation are typically delivered over many sessions in contrast to control groups, which often receive “standard care” delivered over a single or limited number of sessions. Therefore, where an effect exists it is unclear whether the “active ingredient” contributing to the effect is the rehabilitative content delivered or the number of sessions over which it is provided. This is a possible confounding factor not acknowledged or discussed in these systematic reviews.

In addition, any discussion which seeks to assess effectiveness should define outcomes of interest or at least review the range of outcomes that have been studied and how they are relevant to the problem being addressed. There is a lack of consensus on what the important clinical outcomes are for hearing health care [9] and a dearth of research on longer-term outcomes; this could have been explored.

Applications for Clinical Practice

This summary of 3 systematic reviews highlights the need for further research in this area. Studies that measure long-term outcomes (1 year or more) and that are appropriately powered are needed. In addition, the possible interaction between different potentially active components of complex interventions must be acknowledged. Health care professionals and policy makers need to be aware of these factors when reviewing evidence and making decisions that impact on clinical practice.

—Fiona Barker, Department of Health Care Management and Policy, University of Surrey, Guildford, UK

Study Overview

Objective. To determine the effectiveness of adult auditory rehabilitation programs that focus on the use of communication strategies.

Design. Nonsystematic review of the literature.

Methods. The authors used the PubMed database to search for systematic reviews investigating the effectiveness of auditory training and auditory rehabilitation programs. Auditory training involves the patient participating in a program of training designed to enhance speech perception. Training is typically provided on a repeated basis over a number of sessions and involves practice with listening and recognition of speech-based material. Auditory rehabilitation programs can be delivered to groups or individuals and usually have a focus on supplementing information about hearing loss and hearing aids with advice and/or practice with communication strategies and the management of psychosocial aspects of hearing loss.

Main outcome measures. A variety of outcomes were measured in the individual studies.

Results. One systematic review of individual auditory training and 2 systematic reviews of group auditory rehabilitation were identified. Sweetow and Palmer [1] found that auditory training in which speech was broken down into its parts was associated with improvements in the perception of speech in the presence of background noise and better use of active listening strategies. With regard to rehabilitation programs, Hawkins [2] found evidence of short-term reduction in perceived hearing handicap but less evidence of effectiveness for other outcomes. A more recent review by Chisolm and Arnold [3] included only randomized studies that examined the effect of the program on social participation and quality of life. References for the included studies were given but the summary results of this review were not included in this paper.

Conclusion. The authors conclude that there is some evidence that auditory rehabilitation programs are associated with improvements in social participation and quality of life but they acknowledge that more evidence is required.

Commentary

Adult acquired hearing loss is a common long-term condition which in the majority of cases is not remediable by surgical or medical intervention. It is the second leading cause of years lived with a disability [4]. Intervention options for people with hearing loss include hearing aid fitting and/or participation in a rehabilitative program that might include information about hearing and communication and practice or experience with listening or communication tasks [5,6]. Poorly managed hearing loss is associated with negative consequences including depression and cognitive decline [7,8]. Identifying effective management options for hearing loss can reduce these consequences and improve quality of life. In the current paper, Cardemil et al review the evidence for the effectiveness of adult auditory rehabilitation. They note that hearing aids alone cannot ameliorate all of the difficulties caused by age-related hearing loss, where cognitive factors play a significant role. They provide a rationale for why communication-based training in auditory rehabilitation has been recommended as a substitute or supplement to hearing aid fitting. That is, such training addresses the cognitive and communication difficulties that exist in addition to the hearing loss.

This paper summarises the findings of 3 systematic reviews: one on individual auditory rehabilitation and 2 on group rehabilitation. There was heterogeneity among the programs and the methods used to assess their effectiveness were variable. The reviewers conclude that there appears to be some short-term benefit to auditory rehabilitation programs but effect sizes, where effects are seen, are small and more research is needed to establish the effectiveness of these programs.

A limitation of this review is that it was nonsystematic and did not critically appraise the quality of the included systematic reviews. One weakness of the systematic reviews presented is that they did not consider interactions between content and delivery of interventions and comparisons. Individual auditory rehabilitation and group rehabilitation are typically delivered over many sessions in contrast to control groups, which often receive “standard care” delivered over a single or limited number of sessions. Therefore, where an effect exists it is unclear whether the “active ingredient” contributing to the effect is the rehabilitative content delivered or the number of sessions over which it is provided. This is a possible confounding factor not acknowledged or discussed in these systematic reviews.

In addition, any discussion which seeks to assess effectiveness should define outcomes of interest or at least review the range of outcomes that have been studied and how they are relevant to the problem being addressed. There is a lack of consensus on what the important clinical outcomes are for hearing health care [9] and a dearth of research on longer-term outcomes; this could have been explored.

Applications for Clinical Practice

This summary of 3 systematic reviews highlights the need for further research in this area. Studies that measure long-term outcomes (1 year or more) and that are appropriately powered are needed. In addition, the possible interaction between different potentially active components of complex interventions must be acknowledged. Health care professionals and policy makers need to be aware of these factors when reviewing evidence and making decisions that impact on clinical practice.

—Fiona Barker, Department of Health Care Management and Policy, University of Surrey, Guildford, UK

Study Overview

Objective. To determine the effectiveness of adult auditory rehabilitation programs that focus on the use of communication strategies.

Design. Nonsystematic review of the literature.

Methods. The authors used the PubMed database to search for systematic reviews investigating the effectiveness of auditory training and auditory rehabilitation programs. Auditory training involves the patient participating in a program of training designed to enhance speech perception. Training is typically provided on a repeated basis over a number of sessions and involves practice with listening and recognition of speech-based material. Auditory rehabilitation programs can be delivered to groups or individuals and usually have a focus on supplementing information about hearing loss and hearing aids with advice and/or practice with communication strategies and the management of psychosocial aspects of hearing loss.

Main outcome measures. A variety of outcomes were measured in the individual studies.

Results. One systematic review of individual auditory training and 2 systematic reviews of group auditory rehabilitation were identified. Sweetow and Palmer [1] found that auditory training in which speech was broken down into its parts was associated with improvements in the perception of speech in the presence of background noise and better use of active listening strategies. With regard to rehabilitation programs, Hawkins [2] found evidence of short-term reduction in perceived hearing handicap but less evidence of effectiveness for other outcomes. A more recent review by Chisolm and Arnold [3] included only randomized studies that examined the effect of the program on social participation and quality of life. References for the included studies were given but the summary results of this review were not included in this paper.

Conclusion. The authors conclude that there is some evidence that auditory rehabilitation programs are associated with improvements in social participation and quality of life but they acknowledge that more evidence is required.

Commentary

Adult acquired hearing loss is a common long-term condition which in the majority of cases is not remediable by surgical or medical intervention. It is the second leading cause of years lived with a disability [4]. Intervention options for people with hearing loss include hearing aid fitting and/or participation in a rehabilitative program that might include information about hearing and communication and practice or experience with listening or communication tasks [5,6]. Poorly managed hearing loss is associated with negative consequences including depression and cognitive decline [7,8]. Identifying effective management options for hearing loss can reduce these consequences and improve quality of life. In the current paper, Cardemil et al review the evidence for the effectiveness of adult auditory rehabilitation. They note that hearing aids alone cannot ameliorate all of the difficulties caused by age-related hearing loss, where cognitive factors play a significant role. They provide a rationale for why communication-based training in auditory rehabilitation has been recommended as a substitute or supplement to hearing aid fitting. That is, such training addresses the cognitive and communication difficulties that exist in addition to the hearing loss.

This paper summarises the findings of 3 systematic reviews: one on individual auditory rehabilitation and 2 on group rehabilitation. There was heterogeneity among the programs and the methods used to assess their effectiveness were variable. The reviewers conclude that there appears to be some short-term benefit to auditory rehabilitation programs but effect sizes, where effects are seen, are small and more research is needed to establish the effectiveness of these programs.

A limitation of this review is that it was nonsystematic and did not critically appraise the quality of the included systematic reviews. One weakness of the systematic reviews presented is that they did not consider interactions between content and delivery of interventions and comparisons. Individual auditory rehabilitation and group rehabilitation are typically delivered over many sessions in contrast to control groups, which often receive “standard care” delivered over a single or limited number of sessions. Therefore, where an effect exists it is unclear whether the “active ingredient” contributing to the effect is the rehabilitative content delivered or the number of sessions over which it is provided. This is a possible confounding factor not acknowledged or discussed in these systematic reviews.

In addition, any discussion which seeks to assess effectiveness should define outcomes of interest or at least review the range of outcomes that have been studied and how they are relevant to the problem being addressed. There is a lack of consensus on what the important clinical outcomes are for hearing health care [9] and a dearth of research on longer-term outcomes; this could have been explored.

Applications for Clinical Practice

This summary of 3 systematic reviews highlights the need for further research in this area. Studies that measure long-term outcomes (1 year or more) and that are appropriately powered are needed. In addition, the possible interaction between different potentially active components of complex interventions must be acknowledged. Health care professionals and policy makers need to be aware of these factors when reviewing evidence and making decisions that impact on clinical practice.

—Fiona Barker, Department of Health Care Management and Policy, University of Surrey, Guildford, UK

Study Overview

Objective. To evaluate the effectiveness of a collaborative telecare intervention on chronic pain management.

Design. Randomized clinical trial.

Settings and participants. Participants were recruited over a 2-year period from 5 primary care clinics within a single Veterans Affairs medical center. Patients aged 18 to 65 years were eligible if they had chronic (≥ 3 months) musculoskeletal pain of at least moderate intensity (Brief Pain Inventory [BPI] score ≥ 5). Patients were  excluded if they had a pending disability claim or a diagnosis of bipolar disorder, schizophrenia, moderately severe cognitive impairment, active suicidal ideation, current illicit drug use or a terminal illness or received primary care outside of the VA. Participants were randomized to either the telephone-delivered collaborative care management intervention group or usual care. Usual care was defined as continuing to receive care from their primary care provider for management of chronic, musculoskeletal pain.

Intervention. The telecare intervention comprised automated symptom monitoring (ASM) and optimized analgesic management through an algorithm-guided stepped care approach delivered by a nurse case manager. ASM was delivered either by an interactive voice-recorded telephone call (51%) or by internet (49%), set according to patient preference. Intervention calls occurred at 1 and 3 months. Additional contact with participants from the intervention group was generated in response to ASM trend reports.

Main outcome measures. The primary outcome was the BPI total score. The BPI scale ranges from 0 to 10, with higher scores indicating worsening pain. A 1-point change is considered clinically important. Secondary pain outcomes included BPI interference and severity, global pain improvement, treatment satisfaction, and use of opioids and other analgesics. Patients were interviewed at 1, 3, 6, and 12 months.

Main results. A total of 250 participants were enrolled, 124 assigned to the intervention group and 126 assigned to usual care. The mean (SD) baseline BPI scores were 5.31 (1.81) for the intervention group and 5.12 (1.80) for usual care. Compared with usual care, the intervention group had a 1.02-point lower BPI score at 12 months (95% confidence interval [CI], −1.58 to −0.47) (P < 0.001). Patients in the intervention group were nearly twice as likely to report at least a 30% improvement in their pain score by 12 months (51.7% vs. 27.1%; relative risk [RR], 1.9 [95% CI, 1.4 to 2.7]), with a number needed to treat of 4.1 (95% CI, 3.0 to 6.4) for a 30% improvement.

Patients in the intervention group were more likely to rate as good to excellent the medication prescribed for their pain (73.9% vs 50.9%; RR, 1.5 [95% CI, 1.2 to 1.8]). Patients in the usual care group were more likely to experience worsening of pain by 6 months compared with the intervention group. A greater number of analgesics were prescribed to patients in the intervention group; however, opioid use between groups did not differ at baseline or at any point during the trial period. For the secondary outcomes, the intervention group reported greater improvement in depression compared with the usual care group, and this difference was statistically significant (P < 0.001). They also reported fewer days of disability (P = 0.34).

Conclusion. Telecare collaborative management was more effective in improving chronic pain outcomes than usual care. This was accomplished through the optimization of non-opioid analgesic therapy facilitated by a stepped care algorithm and automated symptom monitoring.

Commentary

Chronic pain affects up to 116 million American adults and is recognized as an emerging public health problem that costs the United States a half trillion dollars annually, with disability and hospitalization as the largest burdens [1].The physical and psychological complexities of chronic pain require comprehensive individualized care from interdisciplinary teams who will facilitate prevention, treatment, and routine assessment in chronic pain sufferers [2]. However, enhancing pain management in primary care requires overcoming the high costs and considerable time needed to continually support patients in pain. Telecare represents an improved means by which doctors and nurses can provide primary care services to patients in need of comprehensive pain management. However, the effectiveness of interventions delivered to patients suffering from chronic pain, via telecare, is largely unknown.

This study had several strengths, including a distinct and well-defined intervention, population, comparator, and outcome. The inclusion criteria were broad enough to account for various age-groups, and therefore various pain experiences, yet excluded patients with characteristics likely to confound pain outcomes, such as severe mental health disorders. Participants were randomized in blinded fashion to 1 of 2 clearly defined groups. The stepped algorithm used in the study, SCOPE [3], is a validated and reliable method for assessing chronic pain outcomes. The statistical analyses were appropriate and included analyses of variance to detect between-group differences for continuous variables. The rate of follow-up was excellent, with 95% of participants providing measurable outcome assessments at 12 months. The scientific background and rationale for this study were explicit and relevant to current advances in medicine.

The study is not without limitations, however. It is unclear whether the 2 trial groups were treated equally. Data received through ASM from the intervention group prompted physicians to adjust a patient’s medication regimen, essentially providing caregivers updates on a patient’s status. This occurred in addition to the 4 monthly interviews that both groups received per protocol. The study did not elucidate exactly what care was provided to the usual care group and, therefore, does not allow for the disaggregation of the relative effects of optimizing analgesics and continuous provider monitoring. It is difficult to distinguish if additional care or the intervention was more effective in managing pain than usual care. Another limitation, noted by the authors, is the study’s use of a single VA medical center. Demographics reveal a skewed population, 83% male and 77% white, limiting the trial’s generalizability. Most clinical outcomes were considered, though cost-effectiveness of the intervention was not analyzed. As the VA is a cost-sensitive environment, it is important that interventions assessed are not more costly than usual care. Further cost analysis beyond health resource utilization reported in the study would provide a nuanced assessment of telecare’s feasibility as a replacement for usual primary care. Statistically, the study shows significant improvements in chronic pain in those who received the intervention via telecare, therefore, cost analysis is indeed warranted.

Applications for Clinical Practice

This study illuminates the need for a more intensive pain management program that allows for continuous monitoring. Though the intervention was successfully delivered via telecare, further research is needed to assess whether other programs would be as effective when delivered through telecare, and more importantly, to investigate what characteristics of interventions make telecare successful. Telecare has the potential to improve outcomes, reduce costs, and reduce strains on understaffed facilities, though it is still unknown which conditions would gain from this innovation. This study shows that chronic disease, a predominately self-managed condition, would benefit from a more accessible management program [4]. This, however, may not be the case for other health issues, which require continual testing and equipment usage, such as infectious diseases. Further studies should focus on populations that command a patient-centered intervention delivered using a potentially low-cost tool, like the telephone or internet. Finally, a significant cost driver with chronic pain is disability, and though change in disability days was not statistically significant in this trial, patients in the intervention group self-reported a decrease in disability days, where as patients in the usual care group self-reported an increase. A clinical improvement in pain management has the potential to shave millions of dollars from the U.S. economy, this hypothesis deserves further investigation.

—Sara Tierce-Hazard, BA, and Tina Sadarangani, MSN, ANP-BC, GNP-BC

Study Overview

Objective. To evaluate the effectiveness of a collaborative telecare intervention on chronic pain management.

Design. Randomized clinical trial.

Settings and participants. Participants were recruited over a 2-year period from 5 primary care clinics within a single Veterans Affairs medical center. Patients aged 18 to 65 years were eligible if they had chronic (≥ 3 months) musculoskeletal pain of at least moderate intensity (Brief Pain Inventory [BPI] score ≥ 5). Patients were  excluded if they had a pending disability claim or a diagnosis of bipolar disorder, schizophrenia, moderately severe cognitive impairment, active suicidal ideation, current illicit drug use or a terminal illness or received primary care outside of the VA. Participants were randomized to either the telephone-delivered collaborative care management intervention group or usual care. Usual care was defined as continuing to receive care from their primary care provider for management of chronic, musculoskeletal pain.

Intervention. The telecare intervention comprised automated symptom monitoring (ASM) and optimized analgesic management through an algorithm-guided stepped care approach delivered by a nurse case manager. ASM was delivered either by an interactive voice-recorded telephone call (51%) or by internet (49%), set according to patient preference. Intervention calls occurred at 1 and 3 months. Additional contact with participants from the intervention group was generated in response to ASM trend reports.

Main outcome measures. The primary outcome was the BPI total score. The BPI scale ranges from 0 to 10, with higher scores indicating worsening pain. A 1-point change is considered clinically important. Secondary pain outcomes included BPI interference and severity, global pain improvement, treatment satisfaction, and use of opioids and other analgesics. Patients were interviewed at 1, 3, 6, and 12 months.

Main results. A total of 250 participants were enrolled, 124 assigned to the intervention group and 126 assigned to usual care. The mean (SD) baseline BPI scores were 5.31 (1.81) for the intervention group and 5.12 (1.80) for usual care. Compared with usual care, the intervention group had a 1.02-point lower BPI score at 12 months (95% confidence interval [CI], −1.58 to −0.47) (P < 0.001). Patients in the intervention group were nearly twice as likely to report at least a 30% improvement in their pain score by 12 months (51.7% vs. 27.1%; relative risk [RR], 1.9 [95% CI, 1.4 to 2.7]), with a number needed to treat of 4.1 (95% CI, 3.0 to 6.4) for a 30% improvement.

Patients in the intervention group were more likely to rate as good to excellent the medication prescribed for their pain (73.9% vs 50.9%; RR, 1.5 [95% CI, 1.2 to 1.8]). Patients in the usual care group were more likely to experience worsening of pain by 6 months compared with the intervention group. A greater number of analgesics were prescribed to patients in the intervention group; however, opioid use between groups did not differ at baseline or at any point during the trial period. For the secondary outcomes, the intervention group reported greater improvement in depression compared with the usual care group, and this difference was statistically significant (P < 0.001). They also reported fewer days of disability (P = 0.34).

Conclusion. Telecare collaborative management was more effective in improving chronic pain outcomes than usual care. This was accomplished through the optimization of non-opioid analgesic therapy facilitated by a stepped care algorithm and automated symptom monitoring.

Commentary

Chronic pain affects up to 116 million American adults and is recognized as an emerging public health problem that costs the United States a half trillion dollars annually, with disability and hospitalization as the largest burdens [1].The physical and psychological complexities of chronic pain require comprehensive individualized care from interdisciplinary teams who will facilitate prevention, treatment, and routine assessment in chronic pain sufferers [2]. However, enhancing pain management in primary care requires overcoming the high costs and considerable time needed to continually support patients in pain. Telecare represents an improved means by which doctors and nurses can provide primary care services to patients in need of comprehensive pain management. However, the effectiveness of interventions delivered to patients suffering from chronic pain, via telecare, is largely unknown.

This study had several strengths, including a distinct and well-defined intervention, population, comparator, and outcome. The inclusion criteria were broad enough to account for various age-groups, and therefore various pain experiences, yet excluded patients with characteristics likely to confound pain outcomes, such as severe mental health disorders. Participants were randomized in blinded fashion to 1 of 2 clearly defined groups. The stepped algorithm used in the study, SCOPE [3], is a validated and reliable method for assessing chronic pain outcomes. The statistical analyses were appropriate and included analyses of variance to detect between-group differences for continuous variables. The rate of follow-up was excellent, with 95% of participants providing measurable outcome assessments at 12 months. The scientific background and rationale for this study were explicit and relevant to current advances in medicine.

The study is not without limitations, however. It is unclear whether the 2 trial groups were treated equally. Data received through ASM from the intervention group prompted physicians to adjust a patient’s medication regimen, essentially providing caregivers updates on a patient’s status. This occurred in addition to the 4 monthly interviews that both groups received per protocol. The study did not elucidate exactly what care was provided to the usual care group and, therefore, does not allow for the disaggregation of the relative effects of optimizing analgesics and continuous provider monitoring. It is difficult to distinguish if additional care or the intervention was more effective in managing pain than usual care. Another limitation, noted by the authors, is the study’s use of a single VA medical center. Demographics reveal a skewed population, 83% male and 77% white, limiting the trial’s generalizability. Most clinical outcomes were considered, though cost-effectiveness of the intervention was not analyzed. As the VA is a cost-sensitive environment, it is important that interventions assessed are not more costly than usual care. Further cost analysis beyond health resource utilization reported in the study would provide a nuanced assessment of telecare’s feasibility as a replacement for usual primary care. Statistically, the study shows significant improvements in chronic pain in those who received the intervention via telecare, therefore, cost analysis is indeed warranted.

Applications for Clinical Practice

This study illuminates the need for a more intensive pain management program that allows for continuous monitoring. Though the intervention was successfully delivered via telecare, further research is needed to assess whether other programs would be as effective when delivered through telecare, and more importantly, to investigate what characteristics of interventions make telecare successful. Telecare has the potential to improve outcomes, reduce costs, and reduce strains on understaffed facilities, though it is still unknown which conditions would gain from this innovation. This study shows that chronic disease, a predominately self-managed condition, would benefit from a more accessible management program [4]. This, however, may not be the case for other health issues, which require continual testing and equipment usage, such as infectious diseases. Further studies should focus on populations that command a patient-centered intervention delivered using a potentially low-cost tool, like the telephone or internet. Finally, a significant cost driver with chronic pain is disability, and though change in disability days was not statistically significant in this trial, patients in the intervention group self-reported a decrease in disability days, where as patients in the usual care group self-reported an increase. A clinical improvement in pain management has the potential to shave millions of dollars from the U.S. economy, this hypothesis deserves further investigation.

—Sara Tierce-Hazard, BA, and Tina Sadarangani, MSN, ANP-BC, GNP-BC

Study Overview

Objective. To evaluate the effectiveness of a collaborative telecare intervention on chronic pain management.

Design. Randomized clinical trial.

Settings and participants. Participants were recruited over a 2-year period from 5 primary care clinics within a single Veterans Affairs medical center. Patients aged 18 to 65 years were eligible if they had chronic (≥ 3 months) musculoskeletal pain of at least moderate intensity (Brief Pain Inventory [BPI] score ≥ 5). Patients were  excluded if they had a pending disability claim or a diagnosis of bipolar disorder, schizophrenia, moderately severe cognitive impairment, active suicidal ideation, current illicit drug use or a terminal illness or received primary care outside of the VA. Participants were randomized to either the telephone-delivered collaborative care management intervention group or usual care. Usual care was defined as continuing to receive care from their primary care provider for management of chronic, musculoskeletal pain.

Intervention. The telecare intervention comprised automated symptom monitoring (ASM) and optimized analgesic management through an algorithm-guided stepped care approach delivered by a nurse case manager. ASM was delivered either by an interactive voice-recorded telephone call (51%) or by internet (49%), set according to patient preference. Intervention calls occurred at 1 and 3 months. Additional contact with participants from the intervention group was generated in response to ASM trend reports.

Main outcome measures. The primary outcome was the BPI total score. The BPI scale ranges from 0 to 10, with higher scores indicating worsening pain. A 1-point change is considered clinically important. Secondary pain outcomes included BPI interference and severity, global pain improvement, treatment satisfaction, and use of opioids and other analgesics. Patients were interviewed at 1, 3, 6, and 12 months.

Main results. A total of 250 participants were enrolled, 124 assigned to the intervention group and 126 assigned to usual care. The mean (SD) baseline BPI scores were 5.31 (1.81) for the intervention group and 5.12 (1.80) for usual care. Compared with usual care, the intervention group had a 1.02-point lower BPI score at 12 months (95% confidence interval [CI], −1.58 to −0.47) (P < 0.001). Patients in the intervention group were nearly twice as likely to report at least a 30% improvement in their pain score by 12 months (51.7% vs. 27.1%; relative risk [RR], 1.9 [95% CI, 1.4 to 2.7]), with a number needed to treat of 4.1 (95% CI, 3.0 to 6.4) for a 30% improvement.

Patients in the intervention group were more likely to rate as good to excellent the medication prescribed for their pain (73.9% vs 50.9%; RR, 1.5 [95% CI, 1.2 to 1.8]). Patients in the usual care group were more likely to experience worsening of pain by 6 months compared with the intervention group. A greater number of analgesics were prescribed to patients in the intervention group; however, opioid use between groups did not differ at baseline or at any point during the trial period. For the secondary outcomes, the intervention group reported greater improvement in depression compared with the usual care group, and this difference was statistically significant (P < 0.001). They also reported fewer days of disability (P = 0.34).

Conclusion. Telecare collaborative management was more effective in improving chronic pain outcomes than usual care. This was accomplished through the optimization of non-opioid analgesic therapy facilitated by a stepped care algorithm and automated symptom monitoring.

Commentary

Chronic pain affects up to 116 million American adults and is recognized as an emerging public health problem that costs the United States a half trillion dollars annually, with disability and hospitalization as the largest burdens [1].The physical and psychological complexities of chronic pain require comprehensive individualized care from interdisciplinary teams who will facilitate prevention, treatment, and routine assessment in chronic pain sufferers [2]. However, enhancing pain management in primary care requires overcoming the high costs and considerable time needed to continually support patients in pain. Telecare represents an improved means by which doctors and nurses can provide primary care services to patients in need of comprehensive pain management. However, the effectiveness of interventions delivered to patients suffering from chronic pain, via telecare, is largely unknown.

This study had several strengths, including a distinct and well-defined intervention, population, comparator, and outcome. The inclusion criteria were broad enough to account for various age-groups, and therefore various pain experiences, yet excluded patients with characteristics likely to confound pain outcomes, such as severe mental health disorders. Participants were randomized in blinded fashion to 1 of 2 clearly defined groups. The stepped algorithm used in the study, SCOPE [3], is a validated and reliable method for assessing chronic pain outcomes. The statistical analyses were appropriate and included analyses of variance to detect between-group differences for continuous variables. The rate of follow-up was excellent, with 95% of participants providing measurable outcome assessments at 12 months. The scientific background and rationale for this study were explicit and relevant to current advances in medicine.

The study is not without limitations, however. It is unclear whether the 2 trial groups were treated equally. Data received through ASM from the intervention group prompted physicians to adjust a patient’s medication regimen, essentially providing caregivers updates on a patient’s status. This occurred in addition to the 4 monthly interviews that both groups received per protocol. The study did not elucidate exactly what care was provided to the usual care group and, therefore, does not allow for the disaggregation of the relative effects of optimizing analgesics and continuous provider monitoring. It is difficult to distinguish if additional care or the intervention was more effective in managing pain than usual care. Another limitation, noted by the authors, is the study’s use of a single VA medical center. Demographics reveal a skewed population, 83% male and 77% white, limiting the trial’s generalizability. Most clinical outcomes were considered, though cost-effectiveness of the intervention was not analyzed. As the VA is a cost-sensitive environment, it is important that interventions assessed are not more costly than usual care. Further cost analysis beyond health resource utilization reported in the study would provide a nuanced assessment of telecare’s feasibility as a replacement for usual primary care. Statistically, the study shows significant improvements in chronic pain in those who received the intervention via telecare, therefore, cost analysis is indeed warranted.

Applications for Clinical Practice

This study illuminates the need for a more intensive pain management program that allows for continuous monitoring. Though the intervention was successfully delivered via telecare, further research is needed to assess whether other programs would be as effective when delivered through telecare, and more importantly, to investigate what characteristics of interventions make telecare successful. Telecare has the potential to improve outcomes, reduce costs, and reduce strains on understaffed facilities, though it is still unknown which conditions would gain from this innovation. This study shows that chronic disease, a predominately self-managed condition, would benefit from a more accessible management program [4]. This, however, may not be the case for other health issues, which require continual testing and equipment usage, such as infectious diseases. Further studies should focus on populations that command a patient-centered intervention delivered using a potentially low-cost tool, like the telephone or internet. Finally, a significant cost driver with chronic pain is disability, and though change in disability days was not statistically significant in this trial, patients in the intervention group self-reported a decrease in disability days, where as patients in the usual care group self-reported an increase. A clinical improvement in pain management has the potential to shave millions of dollars from the U.S. economy, this hypothesis deserves further investigation.

—Sara Tierce-Hazard, BA, and Tina Sadarangani, MSN, ANP-BC, GNP-BC

Study Overview

Objective. To compare the efficacy of non-nutritive sweetened beverages (NNS) or water for weight loss during a 12-week behavioral weight loss treatment program.

Study design. 2-arm equivalence randomized clinical trial.

Setting and participants. Participants were recruited at the University of Colorado and Temple University. A total of 506 participants were screened and 308 were enrolled in the study. Inclusion criteria included being weight stable within 10 pounds in the 6 months prior to the trial, engaging in fewer than 300 min of physical activity per week and consuming at least 3 NNS beverages per week. Exclusion criteria included pregnancy, diabetes, cardiovascular disease, uncontrolled hypertension, and the use of medications affecting metabolism or weight. Participants also had physician approval stating they were in good health and could handle the nutrition and exercise requirements of the trial. Participants were randomly assigned to a NNS or water treatment arm using a computer-generated randomization that equally distributed men and women between the 2 groups. Participants had to be willing to discontinue consumption of NNS beverages for the duration of the 1-year study if they were randomized to the water-only group.

Intervention. The study was designed to include a 12-week weight loss phase followed by a 9-month maintenance phase. All participants received a cognitive-behavioral weight loss intervention called The Colorado Weigh. The program involved weekly hour-long group meetings led by registered dieticians or clinical psychologists. Groups were split by research arm and participants were taught about different weight loss strategies including self-monitoring, portion sizes, and physical activity. Participants were weighed at each meeting. The group curriculum was the same for both arms of the study except in the type of beverage they were encouraged to consume.

Participants were given individual energy targets based on their estimated resting metabolic rate (RMR), determined by using a Tanita Model TBF-300A bioelectrical impedance device that assesses body composition. Group leaders adjusted these targets as needed for participants in order to achieve a goal weight loss of 1 to 2 pounds per week. Physical activity targets were set to increase each participant’s typical physical activity by 10 minutes a week with a final target of 60 minutes a day, 6 days a week. Participants filled out daily exercise logs. Additionally, physical activity was assessed by the use of a Body Media armband that participants wore weeks 1 and 12.

Participants in the NNS group were asked to consume at least 24 fluid ounces of NNS beverage per day. Their water consumption was not limited. A beverage was considered NNS if it had less than 5 kcal per 8-ounce serving, was pre-mixed, and contained non-nutritive sweeteners. Participants in the water-only group were asked to drink at least 24 ounces of water a day and not drink any NNS beverages. They were allowed to eat foods that contained NNS but could not intentionally add NNS to beverages such as coffee. Participants in both groups were asked to record their beverage intake daily. Participants were given manufacturers’ coupons for bottled water or NNS beverages.

Main outcome measures. The primary outcomes were weight loss at 12 weeks (weight loss period) and at 1 year (weight loss maintenance). All assessments were conducted at baseline and after 12 weeks. This was designed as an equivalence trial, and the authors’ hypothesis was that there would be no clinically meaningful difference in weight change between the 2 groups. The authors pre-specified that the bounds of equivalence would be 1.7 kg. Waist circumference was recorded in addition to height and weight. Participant’s blood pressure was also recorded and blood samples were collected to measure lipids and glucose. Urine samples were collected to measure urine osmolality. Participants completed questionnaires at baseline and 12 weeks to assess changes in perceived hunger.

Results. A total of 308 patients were randomized following baseline assessment but 5 did not begin treatment. 279 of the remaining 303 participants completed the full 12-week weight loss phase of the study. The dropout rate in the water group was 10% compared to 5.8% in the NNS group, but this was not statistically significant. 80% of participants were female, 68% were white, and 27% African American. There were no significant differences at baseline in age, gender, race/ethnicity or other measures between the water-only and NNS groups. There was no significant difference in adherence to the beverage requirements between the 2 groups (96.6% in the NNS group and 95.7% in the water-only group), and similarly group attendance did not differ between the 2 groups (90.8% for NNS and 89.7% for water-only).

The mean weight loss difference between the water and NNS groups was –1.85 kg (90% confidence interval [CI], –1.12 to –2.58 kg). Because the lower confidence limit of –2.58 kg was outside the equivalence limit set in the hypothesis, the 2 treatments were not considered equivalent and paired comparisons were carried out. Analysis done using an intention-to-treat scheme indicated that the weight loss in the NNS group (5.95 kg ± 3.94 kg) was significantly higher than the weight loss in the water-only group (4.09 ± 3.74 kg, P < 0.001). 43.0% of participants in the water-only group lost > 5% of their body weight and 64.3% of participants in the NNS group lost > 5% of their body weight (P < 0.001).

After 12 weeks of treatment there was no significant difference between the 2 groups in changes in waist circumference, blood pressure, HDL, triglycerides, or urine osmolality. Reductions in total cholesterol and LDL were significantly greater in the NNS group than the water group. There were no significant changes in physical activity between the 2 groups as measured by the exercise logs or the Body Media armbands. There was a statistically significant difference in hunger between the 2 groups (P = 0.013): participants in the water group reported increased hunger, while participants in the NNS group reported a slight decrease in hunger.

Conclusion. Participants who drank at least 3 servings of NNS beverages a day at baseline lost more weight during a behavioral weight loss program when they continued to drink NNS beverages than participants who were asked to cut NNS beverages and drink only water. The study was designed as an equivalence trial but paired comparisons showed a significant difference in weight loss between the 2 groups.

Commentary

Obesity is a major public health concern in the United States and drinking sugar-sweetened beverages has been indicated as a significant contributing factor. Consumption of sugar-sweetened beverages increased considerably from 1994 to 2004 [1]. Fortunately, there is strong evidence that decreasing the consumption of sugar sweetened beverages can lead to weight loss [2]. Most studies look at the effect of replacing sugar-sweetened beverages with water [3] and, in fact, increased consumption of water has been shown to aid weight loss [4]. The relationship between diet drinks and obesity, however, has been a source of controversy. Since NNS beverages contain little to no calories they are a logical replacement for sugar-sweetened beverages, but observational studies have shown a positive correlation between diet drinks and obesity [5,6] as well as type 2 diabetes [7]. Additionally, a recent study by Suez et al [8] found that consumption of artificial sweeteners affects the gut microbiota and increases glucose intolerance. However this correlation may not be causal; NNS beverage consumption may be higher in overweight individuals. A study by Tate et al [9,10] looked at replacing sugar-sweetened beverages with water or artificially sweetened beverages and found no significant difference in weight loss between the 2 groups. However, the Tate et al study used beverage replacement as the primary intervention. This experiment by Peters et al is unique because it tested the hypothesis that NNS is equivalent to water alone when combined with a structured weight loss program. Their results reject the equivalence hypothesis and suggest that NNS beverages facilitate weight loss for patients already consuming them.

Strengths of this study included the use of a randomized, equivalence design. The study also examined secondary outcomes (eg, waist circumference, lipids, and urine osmolality) that helped reinforce that participants consuming NNS were able to lose weight without compromising their health. Further, they measured hunger and found that participants in the NNS beverage group had decreased hunger while those in the water group had increased hunger, which points to a potential mechanism for their findings.

However, the potential for bias in this study is concerning. One major weakness is that all the participants were initially regular drinkers of NNS beverages. The authors never explain why consuming 3 NNS beverages per week was an inclusion criteria. Participants in the water group had to change their behavior to abstain from NNS beverages and this may have impacted results. More concerning, this study was fully funded by the American Beverage Association, who has an obvious interest in promoting NNS beverage consumption. Finally, the authors mention that 5 participants dropped out after randomization but before the start of treatment and were excluded from the study after baseline assessment. The authors do not provide information about group allocation or if the participants knew which group they were assigned to, calling into question the integrity of the intention-to-treat design.

Applications for Clinical Practice

For patients who already drink NNS beverages and are motivated to lose weight, these results support continued use. However, it is unclear how NNS beverages impact weight loss efforts for patients who do not currently drink them. Further, since other studies have shown potential harm of NNS beverages [6–8], more studies are needed to better elucidate their health effects.

—Susan Creighton and Melanie Jay, MD, MS

Study Overview

Objective. To compare the efficacy of non-nutritive sweetened beverages (NNS) or water for weight loss during a 12-week behavioral weight loss treatment program.

Study design. 2-arm equivalence randomized clinical trial.

Setting and participants. Participants were recruited at the University of Colorado and Temple University. A total of 506 participants were screened and 308 were enrolled in the study. Inclusion criteria included being weight stable within 10 pounds in the 6 months prior to the trial, engaging in fewer than 300 min of physical activity per week and consuming at least 3 NNS beverages per week. Exclusion criteria included pregnancy, diabetes, cardiovascular disease, uncontrolled hypertension, and the use of medications affecting metabolism or weight. Participants also had physician approval stating they were in good health and could handle the nutrition and exercise requirements of the trial. Participants were randomly assigned to a NNS or water treatment arm using a computer-generated randomization that equally distributed men and women between the 2 groups. Participants had to be willing to discontinue consumption of NNS beverages for the duration of the 1-year study if they were randomized to the water-only group.

Intervention. The study was designed to include a 12-week weight loss phase followed by a 9-month maintenance phase. All participants received a cognitive-behavioral weight loss intervention called The Colorado Weigh. The program involved weekly hour-long group meetings led by registered dieticians or clinical psychologists. Groups were split by research arm and participants were taught about different weight loss strategies including self-monitoring, portion sizes, and physical activity. Participants were weighed at each meeting. The group curriculum was the same for both arms of the study except in the type of beverage they were encouraged to consume.

Participants were given individual energy targets based on their estimated resting metabolic rate (RMR), determined by using a Tanita Model TBF-300A bioelectrical impedance device that assesses body composition. Group leaders adjusted these targets as needed for participants in order to achieve a goal weight loss of 1 to 2 pounds per week. Physical activity targets were set to increase each participant’s typical physical activity by 10 minutes a week with a final target of 60 minutes a day, 6 days a week. Participants filled out daily exercise logs. Additionally, physical activity was assessed by the use of a Body Media armband that participants wore weeks 1 and 12.

Participants in the NNS group were asked to consume at least 24 fluid ounces of NNS beverage per day. Their water consumption was not limited. A beverage was considered NNS if it had less than 5 kcal per 8-ounce serving, was pre-mixed, and contained non-nutritive sweeteners. Participants in the water-only group were asked to drink at least 24 ounces of water a day and not drink any NNS beverages. They were allowed to eat foods that contained NNS but could not intentionally add NNS to beverages such as coffee. Participants in both groups were asked to record their beverage intake daily. Participants were given manufacturers’ coupons for bottled water or NNS beverages.

Main outcome measures. The primary outcomes were weight loss at 12 weeks (weight loss period) and at 1 year (weight loss maintenance). All assessments were conducted at baseline and after 12 weeks. This was designed as an equivalence trial, and the authors’ hypothesis was that there would be no clinically meaningful difference in weight change between the 2 groups. The authors pre-specified that the bounds of equivalence would be 1.7 kg. Waist circumference was recorded in addition to height and weight. Participant’s blood pressure was also recorded and blood samples were collected to measure lipids and glucose. Urine samples were collected to measure urine osmolality. Participants completed questionnaires at baseline and 12 weeks to assess changes in perceived hunger.

Results. A total of 308 patients were randomized following baseline assessment but 5 did not begin treatment. 279 of the remaining 303 participants completed the full 12-week weight loss phase of the study. The dropout rate in the water group was 10% compared to 5.8% in the NNS group, but this was not statistically significant. 80% of participants were female, 68% were white, and 27% African American. There were no significant differences at baseline in age, gender, race/ethnicity or other measures between the water-only and NNS groups. There was no significant difference in adherence to the beverage requirements between the 2 groups (96.6% in the NNS group and 95.7% in the water-only group), and similarly group attendance did not differ between the 2 groups (90.8% for NNS and 89.7% for water-only).

The mean weight loss difference between the water and NNS groups was –1.85 kg (90% confidence interval [CI], –1.12 to –2.58 kg). Because the lower confidence limit of –2.58 kg was outside the equivalence limit set in the hypothesis, the 2 treatments were not considered equivalent and paired comparisons were carried out. Analysis done using an intention-to-treat scheme indicated that the weight loss in the NNS group (5.95 kg ± 3.94 kg) was significantly higher than the weight loss in the water-only group (4.09 ± 3.74 kg, P < 0.001). 43.0% of participants in the water-only group lost > 5% of their body weight and 64.3% of participants in the NNS group lost > 5% of their body weight (P < 0.001).

After 12 weeks of treatment there was no significant difference between the 2 groups in changes in waist circumference, blood pressure, HDL, triglycerides, or urine osmolality. Reductions in total cholesterol and LDL were significantly greater in the NNS group than the water group. There were no significant changes in physical activity between the 2 groups as measured by the exercise logs or the Body Media armbands. There was a statistically significant difference in hunger between the 2 groups (P = 0.013): participants in the water group reported increased hunger, while participants in the NNS group reported a slight decrease in hunger.

Conclusion. Participants who drank at least 3 servings of NNS beverages a day at baseline lost more weight during a behavioral weight loss program when they continued to drink NNS beverages than participants who were asked to cut NNS beverages and drink only water. The study was designed as an equivalence trial but paired comparisons showed a significant difference in weight loss between the 2 groups.

Commentary

Obesity is a major public health concern in the United States and drinking sugar-sweetened beverages has been indicated as a significant contributing factor. Consumption of sugar-sweetened beverages increased considerably from 1994 to 2004 [1]. Fortunately, there is strong evidence that decreasing the consumption of sugar sweetened beverages can lead to weight loss [2]. Most studies look at the effect of replacing sugar-sweetened beverages with water [3] and, in fact, increased consumption of water has been shown to aid weight loss [4]. The relationship between diet drinks and obesity, however, has been a source of controversy. Since NNS beverages contain little to no calories they are a logical replacement for sugar-sweetened beverages, but observational studies have shown a positive correlation between diet drinks and obesity [5,6] as well as type 2 diabetes [7]. Additionally, a recent study by Suez et al [8] found that consumption of artificial sweeteners affects the gut microbiota and increases glucose intolerance. However this correlation may not be causal; NNS beverage consumption may be higher in overweight individuals. A study by Tate et al [9,10] looked at replacing sugar-sweetened beverages with water or artificially sweetened beverages and found no significant difference in weight loss between the 2 groups. However, the Tate et al study used beverage replacement as the primary intervention. This experiment by Peters et al is unique because it tested the hypothesis that NNS is equivalent to water alone when combined with a structured weight loss program. Their results reject the equivalence hypothesis and suggest that NNS beverages facilitate weight loss for patients already consuming them.

Strengths of this study included the use of a randomized, equivalence design. The study also examined secondary outcomes (eg, waist circumference, lipids, and urine osmolality) that helped reinforce that participants consuming NNS were able to lose weight without compromising their health. Further, they measured hunger and found that participants in the NNS beverage group had decreased hunger while those in the water group had increased hunger, which points to a potential mechanism for their findings.

However, the potential for bias in this study is concerning. One major weakness is that all the participants were initially regular drinkers of NNS beverages. The authors never explain why consuming 3 NNS beverages per week was an inclusion criteria. Participants in the water group had to change their behavior to abstain from NNS beverages and this may have impacted results. More concerning, this study was fully funded by the American Beverage Association, who has an obvious interest in promoting NNS beverage consumption. Finally, the authors mention that 5 participants dropped out after randomization but before the start of treatment and were excluded from the study after baseline assessment. The authors do not provide information about group allocation or if the participants knew which group they were assigned to, calling into question the integrity of the intention-to-treat design.

Applications for Clinical Practice

For patients who already drink NNS beverages and are motivated to lose weight, these results support continued use. However, it is unclear how NNS beverages impact weight loss efforts for patients who do not currently drink them. Further, since other studies have shown potential harm of NNS beverages [6–8], more studies are needed to better elucidate their health effects.

—Susan Creighton and Melanie Jay, MD, MS

Study Overview

Objective. To compare the efficacy of non-nutritive sweetened beverages (NNS) or water for weight loss during a 12-week behavioral weight loss treatment program.

Study design. 2-arm equivalence randomized clinical trial.

Setting and participants. Participants were recruited at the University of Colorado and Temple University. A total of 506 participants were screened and 308 were enrolled in the study. Inclusion criteria included being weight stable within 10 pounds in the 6 months prior to the trial, engaging in fewer than 300 min of physical activity per week and consuming at least 3 NNS beverages per week. Exclusion criteria included pregnancy, diabetes, cardiovascular disease, uncontrolled hypertension, and the use of medications affecting metabolism or weight. Participants also had physician approval stating they were in good health and could handle the nutrition and exercise requirements of the trial. Participants were randomly assigned to a NNS or water treatment arm using a computer-generated randomization that equally distributed men and women between the 2 groups. Participants had to be willing to discontinue consumption of NNS beverages for the duration of the 1-year study if they were randomized to the water-only group.

Intervention. The study was designed to include a 12-week weight loss phase followed by a 9-month maintenance phase. All participants received a cognitive-behavioral weight loss intervention called The Colorado Weigh. The program involved weekly hour-long group meetings led by registered dieticians or clinical psychologists. Groups were split by research arm and participants were taught about different weight loss strategies including self-monitoring, portion sizes, and physical activity. Participants were weighed at each meeting. The group curriculum was the same for both arms of the study except in the type of beverage they were encouraged to consume.

Participants were given individual energy targets based on their estimated resting metabolic rate (RMR), determined by using a Tanita Model TBF-300A bioelectrical impedance device that assesses body composition. Group leaders adjusted these targets as needed for participants in order to achieve a goal weight loss of 1 to 2 pounds per week. Physical activity targets were set to increase each participant’s typical physical activity by 10 minutes a week with a final target of 60 minutes a day, 6 days a week. Participants filled out daily exercise logs. Additionally, physical activity was assessed by the use of a Body Media armband that participants wore weeks 1 and 12.

Participants in the NNS group were asked to consume at least 24 fluid ounces of NNS beverage per day. Their water consumption was not limited. A beverage was considered NNS if it had less than 5 kcal per 8-ounce serving, was pre-mixed, and contained non-nutritive sweeteners. Participants in the water-only group were asked to drink at least 24 ounces of water a day and not drink any NNS beverages. They were allowed to eat foods that contained NNS but could not intentionally add NNS to beverages such as coffee. Participants in both groups were asked to record their beverage intake daily. Participants were given manufacturers’ coupons for bottled water or NNS beverages.

Main outcome measures. The primary outcomes were weight loss at 12 weeks (weight loss period) and at 1 year (weight loss maintenance). All assessments were conducted at baseline and after 12 weeks. This was designed as an equivalence trial, and the authors’ hypothesis was that there would be no clinically meaningful difference in weight change between the 2 groups. The authors pre-specified that the bounds of equivalence would be 1.7 kg. Waist circumference was recorded in addition to height and weight. Participant’s blood pressure was also recorded and blood samples were collected to measure lipids and glucose. Urine samples were collected to measure urine osmolality. Participants completed questionnaires at baseline and 12 weeks to assess changes in perceived hunger.

Results. A total of 308 patients were randomized following baseline assessment but 5 did not begin treatment. 279 of the remaining 303 participants completed the full 12-week weight loss phase of the study. The dropout rate in the water group was 10% compared to 5.8% in the NNS group, but this was not statistically significant. 80% of participants were female, 68% were white, and 27% African American. There were no significant differences at baseline in age, gender, race/ethnicity or other measures between the water-only and NNS groups. There was no significant difference in adherence to the beverage requirements between the 2 groups (96.6% in the NNS group and 95.7% in the water-only group), and similarly group attendance did not differ between the 2 groups (90.8% for NNS and 89.7% for water-only).

The mean weight loss difference between the water and NNS groups was –1.85 kg (90% confidence interval [CI], –1.12 to –2.58 kg). Because the lower confidence limit of –2.58 kg was outside the equivalence limit set in the hypothesis, the 2 treatments were not considered equivalent and paired comparisons were carried out. Analysis done using an intention-to-treat scheme indicated that the weight loss in the NNS group (5.95 kg ± 3.94 kg) was significantly higher than the weight loss in the water-only group (4.09 ± 3.74 kg, P < 0.001). 43.0% of participants in the water-only group lost > 5% of their body weight and 64.3% of participants in the NNS group lost > 5% of their body weight (P < 0.001).

After 12 weeks of treatment there was no significant difference between the 2 groups in changes in waist circumference, blood pressure, HDL, triglycerides, or urine osmolality. Reductions in total cholesterol and LDL were significantly greater in the NNS group than the water group. There were no significant changes in physical activity between the 2 groups as measured by the exercise logs or the Body Media armbands. There was a statistically significant difference in hunger between the 2 groups (P = 0.013): participants in the water group reported increased hunger, while participants in the NNS group reported a slight decrease in hunger.

Conclusion. Participants who drank at least 3 servings of NNS beverages a day at baseline lost more weight during a behavioral weight loss program when they continued to drink NNS beverages than participants who were asked to cut NNS beverages and drink only water. The study was designed as an equivalence trial but paired comparisons showed a significant difference in weight loss between the 2 groups.

Commentary

Obesity is a major public health concern in the United States and drinking sugar-sweetened beverages has been indicated as a significant contributing factor. Consumption of sugar-sweetened beverages increased considerably from 1994 to 2004 [1]. Fortunately, there is strong evidence that decreasing the consumption of sugar sweetened beverages can lead to weight loss [2]. Most studies look at the effect of replacing sugar-sweetened beverages with water [3] and, in fact, increased consumption of water has been shown to aid weight loss [4]. The relationship between diet drinks and obesity, however, has been a source of controversy. Since NNS beverages contain little to no calories they are a logical replacement for sugar-sweetened beverages, but observational studies have shown a positive correlation between diet drinks and obesity [5,6] as well as type 2 diabetes [7]. Additionally, a recent study by Suez et al [8] found that consumption of artificial sweeteners affects the gut microbiota and increases glucose intolerance. However this correlation may not be causal; NNS beverage consumption may be higher in overweight individuals. A study by Tate et al [9,10] looked at replacing sugar-sweetened beverages with water or artificially sweetened beverages and found no significant difference in weight loss between the 2 groups. However, the Tate et al study used beverage replacement as the primary intervention. This experiment by Peters et al is unique because it tested the hypothesis that NNS is equivalent to water alone when combined with a structured weight loss program. Their results reject the equivalence hypothesis and suggest that NNS beverages facilitate weight loss for patients already consuming them.

Strengths of this study included the use of a randomized, equivalence design. The study also examined secondary outcomes (eg, waist circumference, lipids, and urine osmolality) that helped reinforce that participants consuming NNS were able to lose weight without compromising their health. Further, they measured hunger and found that participants in the NNS beverage group had decreased hunger while those in the water group had increased hunger, which points to a potential mechanism for their findings.

However, the potential for bias in this study is concerning. One major weakness is that all the participants were initially regular drinkers of NNS beverages. The authors never explain why consuming 3 NNS beverages per week was an inclusion criteria. Participants in the water group had to change their behavior to abstain from NNS beverages and this may have impacted results. More concerning, this study was fully funded by the American Beverage Association, who has an obvious interest in promoting NNS beverage consumption. Finally, the authors mention that 5 participants dropped out after randomization but before the start of treatment and were excluded from the study after baseline assessment. The authors do not provide information about group allocation or if the participants knew which group they were assigned to, calling into question the integrity of the intention-to-treat design.

Applications for Clinical Practice

For patients who already drink NNS beverages and are motivated to lose weight, these results support continued use. However, it is unclear how NNS beverages impact weight loss efforts for patients who do not currently drink them. Further, since other studies have shown potential harm of NNS beverages [6–8], more studies are needed to better elucidate their health effects.

—Susan Creighton and Melanie Jay, MD, MS

Study Overview

Objective. To investigate the feasibility and effectiveness of administering diabetes self-management support (DSMS) via telephone or secure messaging.

Design. Prospective, longitudinal quasi-experimental study.

Setting and participants. Participants (n = 150) who had previously completed diabetes self-management education (DSME) received follow-up DSMS in 1 of 3 self-selected ways: a one-time in-person visit, 3 brief visits by telephone, or via secure messaging via the electronic health record. The (usual care) in-person group (n = 47) received 1 follow-up appointment at the patient’s request with a certified diabetes educator (CDE) within 3 to 6 months of DSME completion. The telephone group (n = 44) was given follow-up phone appointments with a CDE, each lasting approximately 20 minutes, at 3, 6, and 9 months post-DSME. The secure message group (n = 59) received follow-up messages via the patient portal from a CDE at 3, 6, and 9 months post-DSME. At each interval, patients received 3 messages, an initial one followed by 2 structured replies. Motivational interviewing techniques were used in all 3 groups to identify barriers to achieving behavior goals and solutions.

Main outcome measures. Behavior goal measures, feasibility measures, and physiologic measures at 9 months’ post DSME. Behavior goal achievement was measured using a survey that asked patients to rate their achievement regarding the following AADE7 goals: healthy eating, being active, self-monitoring, taking medications, problem solving, reducing risks, and healthy coping. Goals are rated on a scale from 0 to 10, with a rating ≥ 7 considered successful completion. Feasibility to integrate this technology into a DSME platform was assessed by comparing the number of attempts to contact patients with the number of contacts achieved; also calculated was intervention completion, mean time spent with the CDE, and total cost of each visit. Physiologic measures included HbA1C and LDL levels collected through medical record review.

Results. There were no statistically significant differences between groups with respect to any of the primary outcomes. Behavioral goals were achieved by 59% of the in-person group, 73% of the telephone group, and 77% of the secure message group . Mean goal achievement for all 3 groups combined improved from 6.2 ± 2.4 to 7.2 ± 1.8 (P < 0.05). Overall, 70.3% ± 0.46% achieved behavioral goals, with no difference among groups. In terms of feasibility, at 3 months the contact success rate was 39%, 46%, and 29% in the in-person, telephone, and secure message groups, respectively. At 6 months, the contact success rate was 47% in the phone group versus 32% in the secure message group. At 9 months, the contact success rate was 35% in the phone group versus 21% in the secure message group. Sixty-two participants (41%) completed the intervention per protocol: 51% of in-person patients, 47% of phone patients, and 28% of secure message patients (P < 0.02). Visits lasted and cost, on average, 60 minutes and $50.00, 45.3 minutes and $37.75, and 17.8 minutes (P < 0.05) and $14.83 for the in-person, telephone, and secure message groups, respectively. There was no difference in HbA1c among groups. Overall, HbA1c decreased by −0.88% ± 1.63 (P < 0.05) from baseline to 9 months. Change in LDL was not significant, and neither were there statistical differences among groups.

Conclusion. Diabetes follow-up care delivered via telephone and secure messaging is feasible. Using either of these methods results in similar outcomes compared with the traditional in-person visit, while requiring less staff time.

Commentary

Diabetes mellitus is a growing epidemic in the United States, affecting nearly 10% of American adults [1]. The disease is associated with multiple, potentially fatal complications, including heart disease, stroke, kidney failure, and limb amputation [1]. Studies show that ongoing diabetes self-management education (DSME) can result in lifestyle and behavioral changes that improve glycemic control, ultimately reducing the risk of complications [2,3]. However, traditional follow-up care and education for patients with diabetes requires considerable time on the part of patients and providers, and is both costly and resource-intensive [4]. The use of telehealth to educate and monitor patients with diabetes is a growing phenomenon. Theoretically, telehealth enables providers to reach greater subsets of the population who may not otherwise be able to consult with a doctor or nurse regularly. However, little is known about the overall effectiveness of telehealth compared with regular office visits with respect to diabetes and patient outcomes.

This study investigated the feasibility of using telephone and secure message methods to deliver ongoing DSMS after the completion of an existing DSME program. The results suggest that there is no difference in behavioral goal achievement, feasibility, and clinical outcomes among usual care and intervention groups.

This study had a number of strengths, including a strong scientific background in support of research that examines telehealth options for diabetes management. The inclusion criteria were straightforward and appropriate for the targeted patient population: all participants were over 18 and had previously completed the DSME class; all participants in the phone group were required to have a working telephone line, while the secure message group participants were required to have internet access. However, there were some methodologic weaknesses, most of which were pointed out by the authors. These included (1) lack of randomization, (2) a high attrition rate, and (3) nonspecific outcome measures. In addition, participants were able to self-enroll into a category of their choice. The lack of randomization enabled selection bias and prohibits the authors from inferring a causal effect between DSMS and improved health outcomes. Attrition rates were also problematic in this study. Not only did 59% of enrolled participants fail to complete the intervention, but the overall contact success rates declined over time. Finally, the outcome measure for feasibility is poorly defined because the authors never provide a numerical measure limitation. External validity is limited by a largely Caucasian sample that is predominately female. Due to the weaknesses inherent in the study’s methodology, the findings should be interpreted with some degree of caution.

Applications for Clinical Practice

Given the potential for long-term complications from diabetes, the rising cost of health care services, and the overall shortage of medical and nursing personnel, alternative methods of patient follow-up are needed in the management of diabetes. Telehealth has the potential to reach a significant portion of the population that is receiving little or no care in rural and underserved areas in a convenient and less costly way than traditional care. Investigating which alternatives to usual care are effective for which patient groups will pave the way for resource optimization and cost-effectiveness. Providing DSME follow-up through telehealth methodologies may be an effective alternative to in-person visits. Additional research is needed to support the outcomes of this study and to determine the duration of DSMS that is needed to ensure sufficient diabetes self-management.

—Amy Burchard, BA, and Tina Sadarangani, MSN, ANP-BC, GNP-BC

Study Overview

Objective. To investigate the feasibility and effectiveness of administering diabetes self-management support (DSMS) via telephone or secure messaging.

Design. Prospective, longitudinal quasi-experimental study.

Setting and participants. Participants (n = 150) who had previously completed diabetes self-management education (DSME) received follow-up DSMS in 1 of 3 self-selected ways: a one-time in-person visit, 3 brief visits by telephone, or via secure messaging via the electronic health record. The (usual care) in-person group (n = 47) received 1 follow-up appointment at the patient’s request with a certified diabetes educator (CDE) within 3 to 6 months of DSME completion. The telephone group (n = 44) was given follow-up phone appointments with a CDE, each lasting approximately 20 minutes, at 3, 6, and 9 months post-DSME. The secure message group (n = 59) received follow-up messages via the patient portal from a CDE at 3, 6, and 9 months post-DSME. At each interval, patients received 3 messages, an initial one followed by 2 structured replies. Motivational interviewing techniques were used in all 3 groups to identify barriers to achieving behavior goals and solutions.

Main outcome measures. Behavior goal measures, feasibility measures, and physiologic measures at 9 months’ post DSME. Behavior goal achievement was measured using a survey that asked patients to rate their achievement regarding the following AADE7 goals: healthy eating, being active, self-monitoring, taking medications, problem solving, reducing risks, and healthy coping. Goals are rated on a scale from 0 to 10, with a rating ≥ 7 considered successful completion. Feasibility to integrate this technology into a DSME platform was assessed by comparing the number of attempts to contact patients with the number of contacts achieved; also calculated was intervention completion, mean time spent with the CDE, and total cost of each visit. Physiologic measures included HbA1C and LDL levels collected through medical record review.

Results. There were no statistically significant differences between groups with respect to any of the primary outcomes. Behavioral goals were achieved by 59% of the in-person group, 73% of the telephone group, and 77% of the secure message group . Mean goal achievement for all 3 groups combined improved from 6.2 ± 2.4 to 7.2 ± 1.8 (P < 0.05). Overall, 70.3% ± 0.46% achieved behavioral goals, with no difference among groups. In terms of feasibility, at 3 months the contact success rate was 39%, 46%, and 29% in the in-person, telephone, and secure message groups, respectively. At 6 months, the contact success rate was 47% in the phone group versus 32% in the secure message group. At 9 months, the contact success rate was 35% in the phone group versus 21% in the secure message group. Sixty-two participants (41%) completed the intervention per protocol: 51% of in-person patients, 47% of phone patients, and 28% of secure message patients (P < 0.02). Visits lasted and cost, on average, 60 minutes and $50.00, 45.3 minutes and $37.75, and 17.8 minutes (P < 0.05) and $14.83 for the in-person, telephone, and secure message groups, respectively. There was no difference in HbA1c among groups. Overall, HbA1c decreased by −0.88% ± 1.63 (P < 0.05) from baseline to 9 months. Change in LDL was not significant, and neither were there statistical differences among groups.

Conclusion. Diabetes follow-up care delivered via telephone and secure messaging is feasible. Using either of these methods results in similar outcomes compared with the traditional in-person visit, while requiring less staff time.

Commentary

Diabetes mellitus is a growing epidemic in the United States, affecting nearly 10% of American adults [1]. The disease is associated with multiple, potentially fatal complications, including heart disease, stroke, kidney failure, and limb amputation [1]. Studies show that ongoing diabetes self-management education (DSME) can result in lifestyle and behavioral changes that improve glycemic control, ultimately reducing the risk of complications [2,3]. However, traditional follow-up care and education for patients with diabetes requires considerable time on the part of patients and providers, and is both costly and resource-intensive [4]. The use of telehealth to educate and monitor patients with diabetes is a growing phenomenon. Theoretically, telehealth enables providers to reach greater subsets of the population who may not otherwise be able to consult with a doctor or nurse regularly. However, little is known about the overall effectiveness of telehealth compared with regular office visits with respect to diabetes and patient outcomes.

This study investigated the feasibility of using telephone and secure message methods to deliver ongoing DSMS after the completion of an existing DSME program. The results suggest that there is no difference in behavioral goal achievement, feasibility, and clinical outcomes among usual care and intervention groups.

This study had a number of strengths, including a strong scientific background in support of research that examines telehealth options for diabetes management. The inclusion criteria were straightforward and appropriate for the targeted patient population: all participants were over 18 and had previously completed the DSME class; all participants in the phone group were required to have a working telephone line, while the secure message group participants were required to have internet access. However, there were some methodologic weaknesses, most of which were pointed out by the authors. These included (1) lack of randomization, (2) a high attrition rate, and (3) nonspecific outcome measures. In addition, participants were able to self-enroll into a category of their choice. The lack of randomization enabled selection bias and prohibits the authors from inferring a causal effect between DSMS and improved health outcomes. Attrition rates were also problematic in this study. Not only did 59% of enrolled participants fail to complete the intervention, but the overall contact success rates declined over time. Finally, the outcome measure for feasibility is poorly defined because the authors never provide a numerical measure limitation. External validity is limited by a largely Caucasian sample that is predominately female. Due to the weaknesses inherent in the study’s methodology, the findings should be interpreted with some degree of caution.

Applications for Clinical Practice

Given the potential for long-term complications from diabetes, the rising cost of health care services, and the overall shortage of medical and nursing personnel, alternative methods of patient follow-up are needed in the management of diabetes. Telehealth has the potential to reach a significant portion of the population that is receiving little or no care in rural and underserved areas in a convenient and less costly way than traditional care. Investigating which alternatives to usual care are effective for which patient groups will pave the way for resource optimization and cost-effectiveness. Providing DSME follow-up through telehealth methodologies may be an effective alternative to in-person visits. Additional research is needed to support the outcomes of this study and to determine the duration of DSMS that is needed to ensure sufficient diabetes self-management.

—Amy Burchard, BA, and Tina Sadarangani, MSN, ANP-BC, GNP-BC

Study Overview

Objective. To investigate the feasibility and effectiveness of administering diabetes self-management support (DSMS) via telephone or secure messaging.

Design. Prospective, longitudinal quasi-experimental study.

Setting and participants. Participants (n = 150) who had previously completed diabetes self-management education (DSME) received follow-up DSMS in 1 of 3 self-selected ways: a one-time in-person visit, 3 brief visits by telephone, or via secure messaging via the electronic health record. The (usual care) in-person group (n = 47) received 1 follow-up appointment at the patient’s request with a certified diabetes educator (CDE) within 3 to 6 months of DSME completion. The telephone group (n = 44) was given follow-up phone appointments with a CDE, each lasting approximately 20 minutes, at 3, 6, and 9 months post-DSME. The secure message group (n = 59) received follow-up messages via the patient portal from a CDE at 3, 6, and 9 months post-DSME. At each interval, patients received 3 messages, an initial one followed by 2 structured replies. Motivational interviewing techniques were used in all 3 groups to identify barriers to achieving behavior goals and solutions.

Main outcome measures. Behavior goal measures, feasibility measures, and physiologic measures at 9 months’ post DSME. Behavior goal achievement was measured using a survey that asked patients to rate their achievement regarding the following AADE7 goals: healthy eating, being active, self-monitoring, taking medications, problem solving, reducing risks, and healthy coping. Goals are rated on a scale from 0 to 10, with a rating ≥ 7 considered successful completion. Feasibility to integrate this technology into a DSME platform was assessed by comparing the number of attempts to contact patients with the number of contacts achieved; also calculated was intervention completion, mean time spent with the CDE, and total cost of each visit. Physiologic measures included HbA1C and LDL levels collected through medical record review.

Results. There were no statistically significant differences between groups with respect to any of the primary outcomes. Behavioral goals were achieved by 59% of the in-person group, 73% of the telephone group, and 77% of the secure message group . Mean goal achievement for all 3 groups combined improved from 6.2 ± 2.4 to 7.2 ± 1.8 (P < 0.05). Overall, 70.3% ± 0.46% achieved behavioral goals, with no difference among groups. In terms of feasibility, at 3 months the contact success rate was 39%, 46%, and 29% in the in-person, telephone, and secure message groups, respectively. At 6 months, the contact success rate was 47% in the phone group versus 32% in the secure message group. At 9 months, the contact success rate was 35% in the phone group versus 21% in the secure message group. Sixty-two participants (41%) completed the intervention per protocol: 51% of in-person patients, 47% of phone patients, and 28% of secure message patients (P < 0.02). Visits lasted and cost, on average, 60 minutes and $50.00, 45.3 minutes and $37.75, and 17.8 minutes (P < 0.05) and $14.83 for the in-person, telephone, and secure message groups, respectively. There was no difference in HbA1c among groups. Overall, HbA1c decreased by −0.88% ± 1.63 (P < 0.05) from baseline to 9 months. Change in LDL was not significant, and neither were there statistical differences among groups.

Conclusion. Diabetes follow-up care delivered via telephone and secure messaging is feasible. Using either of these methods results in similar outcomes compared with the traditional in-person visit, while requiring less staff time.

Commentary

Diabetes mellitus is a growing epidemic in the United States, affecting nearly 10% of American adults [1]. The disease is associated with multiple, potentially fatal complications, including heart disease, stroke, kidney failure, and limb amputation [1]. Studies show that ongoing diabetes self-management education (DSME) can result in lifestyle and behavioral changes that improve glycemic control, ultimately reducing the risk of complications [2,3]. However, traditional follow-up care and education for patients with diabetes requires considerable time on the part of patients and providers, and is both costly and resource-intensive [4]. The use of telehealth to educate and monitor patients with diabetes is a growing phenomenon. Theoretically, telehealth enables providers to reach greater subsets of the population who may not otherwise be able to consult with a doctor or nurse regularly. However, little is known about the overall effectiveness of telehealth compared with regular office visits with respect to diabetes and patient outcomes.

This study investigated the feasibility of using telephone and secure message methods to deliver ongoing DSMS after the completion of an existing DSME program. The results suggest that there is no difference in behavioral goal achievement, feasibility, and clinical outcomes among usual care and intervention groups.

This study had a number of strengths, including a strong scientific background in support of research that examines telehealth options for diabetes management. The inclusion criteria were straightforward and appropriate for the targeted patient population: all participants were over 18 and had previously completed the DSME class; all participants in the phone group were required to have a working telephone line, while the secure message group participants were required to have internet access. However, there were some methodologic weaknesses, most of which were pointed out by the authors. These included (1) lack of randomization, (2) a high attrition rate, and (3) nonspecific outcome measures. In addition, participants were able to self-enroll into a category of their choice. The lack of randomization enabled selection bias and prohibits the authors from inferring a causal effect between DSMS and improved health outcomes. Attrition rates were also problematic in this study. Not only did 59% of enrolled participants fail to complete the intervention, but the overall contact success rates declined over time. Finally, the outcome measure for feasibility is poorly defined because the authors never provide a numerical measure limitation. External validity is limited by a largely Caucasian sample that is predominately female. Due to the weaknesses inherent in the study’s methodology, the findings should be interpreted with some degree of caution.

Applications for Clinical Practice

Given the potential for long-term complications from diabetes, the rising cost of health care services, and the overall shortage of medical and nursing personnel, alternative methods of patient follow-up are needed in the management of diabetes. Telehealth has the potential to reach a significant portion of the population that is receiving little or no care in rural and underserved areas in a convenient and less costly way than traditional care. Investigating which alternatives to usual care are effective for which patient groups will pave the way for resource optimization and cost-effectiveness. Providing DSME follow-up through telehealth methodologies may be an effective alternative to in-person visits. Additional research is needed to support the outcomes of this study and to determine the duration of DSMS that is needed to ensure sufficient diabetes self-management.

—Amy Burchard, BA, and Tina Sadarangani, MSN, ANP-BC, GNP-BC